research paper on demand and supply

Page One Economics ®

The science of supply and demand.

"A body in motion tends to stay in motion unless acted on by an out-side force."  

—Isaac Newton

Science Is Everywhere

We live in a world governed by the laws of science. From gravity, to electromagnetism, to sound waves, our lives are filled with scientific phenomena that structure and affect every facet of our daily routine. As a species, we have attempted at every turn to channel the laws of science to our own benefit, constantly working to build better products and to develop improved means of manufacturing. However, sometimes science unveils itself in unanticipated ways—ways that often force its will on the distribution of goods in markets.

Figure 1 Personal Consumption Expenditures

SOURCE: FRED ® , Federal Reserve Bank of St Louis; https://fred.stlouisfed.org/graph/?g=r60z , accessed January 2021.

Few events demonstrate this fact better than the COVID-19 pandemic of 2020. As this new viral strain spread around the globe, many businesses in the United States closed or reduced workers' hours, sometimes by the choice of businesses—to prevent employees from catching the virus—and sometimes due to government stay-at-home orders. 1 In the early months of the pandemic, virtually no industry or market remained unaffected as the economy declined: Consumer spending on goods and services dropped by 6.7 percent in March and 12.7 percent in April (Figure 1) and the unemployment rate rose from a 50-year low of 3.5 percent in February to a post-Great Depression record of 14.7 percent in April (Figure 2). 

Figure 2 Unemployment Rate

SOURCE: FRED ® , Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/graph/?g=r5AM , accessed January 2021.

Supply and Demand

COVID-19 affected markets the same way they are affected by any outside force—through supply and demand . In competitive markets , supply and demand govern the ways that buyers and sellers determine how much of a good or service to trade in reaction to price changes.

The law of demand describes the behavior of buyers in markets: As the price (P) of a good or service rises, the quantity demanded (Q D ) of that good or service falls. Likewise, as the price of a good or service falls, the quantity demanded of that good or service rises. Consider your favorite snack food. A downward sloping demand curve indicates that as the price of the snack increases, you would be able and/or willing to buy a smaller amount. This relationship is demonstrated by the downward sloping demand curve in Figure 3. When the price increases from P 1 to P 2 , the quantity demanded decreases from Q 1 to Q 2 .

Similarly, the law of supply describes the behavior of sellers in markets: As the price of a good or service rises, the quantity supplied of that good or service rises. Like­wise, as the price of a good or service falls, the quantity supplied of that good or service falls. Therefore, as the price (as determined by the market) of your favorite snack rises, firms are willing to produce more units. This relationship is demonstrated by the graph of the upward sloping supply curve in Figure 4. When the price increases from P 1 to P 2 , firms are willing to supply a greater quantity. That is, the quantity supplied increases from Q 1 to Q 2 .

Market prices are constantly adjusting to bring into balance the amount desired by buyers and the amount sold by sellers. This balance is found at the equilibrium price , where supply and demand intersect (Figure 5). At this point we have our equilibrium price (P e ) and equilibrium quantity (Q e ).

Scientific Events

Biology: COVID-19

The COVID-19 pandemic and the associated lockdowns hit the Leisure and Hospitality sector particularly hard (Figure 6). A recent study looked at hours worked by sector in the immediate aftermath of stay-at-home orders—March 2020. 2 As shown in Figure 6, the effects on hours worked are separated into supply factors (red bars) and demand factors (blue bars) and measured as the percent change in historical growth rates of hours worked in each sector. Supply factors are related to businesses partially or fully shutting down. Demand factors are related to reduced consumer spending, such as from customers not shopping, to avoid catching the virus, or simply cutting back on spending due to income loss. 3 For most sectors, hours worked dropped compared with historical trends due to both supply and demand factors.

When a factor other than price affects supply or demand, it is modeled by shifting the supply or demand curve, respectively, rather than moving along the curve. For increases in supply or demand, the curves are shifted to the right to higher quantities. For decreases, the curves are shifted to the left to lower quantities.

Although supply factors contributed to most of the almost 10 percent drop in the Leisure and Hospitality sector in March 2020 compared with historical growth, demand factors also contributed (see Figure 6). The change in this sector is demonstrated in Figure 7: Demand decreases (shifts to the left) and supply decreases more (also shifts to the left), resulting in a lower quantity of goods sold at the new equilibrium (Q 2 ). 4

Meteorology: Hurricane Sandy

In the fall of 2012, Hurricane Sandy hit New York City and surrounding regions, with millions of citizens and thousands of businesses losing power. In New Jersey, only 40 percent of gas stations tracked by AAA had power and were operational in the immediate aftermath of the hurricane. 5 As a result, consumers faced a severe shock to the supply of gasoline.

Applying the laws of supply and demand, one can predict how this event would change the quantity and price of gasoline at the pump: Assuming unchanged demand, 6 the supply curve would shift to the left (Figure 8). The equilibrium quantity would decrease from Q 1 to Q 2 , with the price increasing from P 1 to P 2 .

Did this occur? Not exactly. New Jersey Governor Chris Christie promised to punish gas stations that significantly increased prices above their pre-hurricane levels (P 1 ). 7 As a result, prices remained low because they were not allowed to reach equilibrium, so oil firms had no incentive to bring extra gasoline to the market at the lower price, long lines of vehicles formed, and many stations sold out due to limited supply. 

Chemistry: The Ethanol Fuel Boom

In the late 2000s, ethanol experienced a boom as an alternative fuel. Compared with gasoline, ethanol was believed to be cleaner burning (produce less carbon dioxide) and could be produced from renewable crops such as corn and sugar cane. 8 With subsidies provided by the U.S. government to produce fuel ethanol, production facilities sprouted up across the Midwest and supply increased in this growing industry. 9

With more and more ethanol being blended into gasoline for use in everyday car engines, many believed that yearly production would continue to grow for years to come. Then, consumers began noticing that their gas engines were being damaged by gasoline mixtures with large percentages of ethanol. 10 As it turns out, the chemical nature of ethanol makes it very attractive to water. When water gets into an engine's fuel, it increases the corrosion of metal and degrades the engine. As a result, regulators decided that gasoline for normal car engines could only contain up to 10 percent ethanol by volume. 11,12

Using supply and demand to analyze fuel ethanol markets is a little tricky due to the volume ethanol limit. In Figure 9, the desire of producers to increase the supply of ethanol is indicated by the rightward shift of the supply curve. Producers would expect ethanol buyers to continue increasing their demand as ethanol becomes more and more popular. However, all else being equal, once buyers are running their vehicles with gasoline with 10 percent ethanol, their desire to purchase more would dramatically decrease and the demand curve would become a nearly straight vertical line. 13 That is, the quantity demanded wouldn't increase much beyond this limit even if the price of ethanol were to decrease because people won't use gasoline with more than 10 percent ethanol. Thus, no matter how much producers wish to increase supply, buyers would not buy much more ethanol and increased production of ethanol would drive down prices.

Figure 10 U.S. Fuel Ethanol Consumption and Percent of Motor Gasoline Consumption, 1981-2019 (June 24, 2020)

Figure 10 confirms this analysis of supply and demand. Fuel ethanol consumption increased dramatically during the 2000s and then flattened out when it reached about 10 percent of motor gasoline consumption. 14

Markets provide a means by which individuals and businesses can trade goods and services. Though goods and services come in many shapes and sizes, they are all governed by the laws of supply and demand. Of course, unanticipated scientific events, such as pandemics and hurricanes, can alter the course of markets. Yet, the same laws that make markets function every day will exert their will—the laws of supply and demand.

https://www.sciencemag.org/news/2020/03/modelers-weigh-value-lives-and-lockdown-costs-put-price-covid-19 .

2 Brinca, Pedro; Duarte, Joao B.  and Faria-e-Castro, Miguel. "Is the COVID-19 Pandemic a Supply or a Demand Shock?" Federal Reserve Bank of St. Louis Economic Synopses , 2020, No. 31; https://research.stlouisfed.org/publications/economic-synopses/2020/05/20/is-the-covid-19-pandemic-a-supply-or-a-demand-shock .

3 Some sectors such as Wholesale Trade and Information were positively impacted by demand factors. In the case of the Information sector, the increase may have been caused by families increasing their demand for goods and services to work, communicate, and/or enjoy entertainment from home.

4 Figure 7 depicts price increasing, but price could decrease depending on the size of the supply and demand shifts and how responsive supply and demand are to price changes. 

5 Smith, Aaron. "Gas Shortage Continues in Areas Hit By Sandy." CNN Business, November 2, 2012; https://money.cnn.com/2012/11/02/news/economy/gas-shortage-sandy/index.html .

6 There could actually have been an increase in demand from individuals using gas powered electric generators during the power outage.

7 Futrelle, David. "Post-Sandy Price Gouging: Economically Sound, Ethically Dubious." Time , November 2, 2012; https://business.time.com/2012/11/02/post-sandy-price-gouging-economically-sound-ethically-dubious/ .

8 U.S. Energy Information Administration. "Biofuels Explained: Ethanol and the Environment." December 7, 2020, update; https://www.eia.gov/energyexplained/biofuels/ethanol-and-the-environment.php .

9 Byrge, Joshua A. and Kliesen, Kevin L. "Ethanol: Economic Gain or Drain?" Federal Reserve Bank of St. Louis Regional Economist , July 1, 2008; https://www.stlouisfed.org/publications/regional-economist/july-2008/ethanol-economic-gain-or-drain .

10 Johnson, M. Alex. "Mechanics See Ethanol Damaging Small Engines." NBC News, August 1, 2008; https://www.nbcnews.com/id/wbna25936782 .

11 Tyner, Wallace E.; Brechbil, Sarah l. and Perkis, David. "Cellulosic Ethanol: Feed­stocks, Conversion Technologies, Economics, and Policy Options." Congressional Research Service, October 22, 2010; http://nationalaglawcenter.org/wp-content/uploads/assets/crs/R41460.pdf .

12 Specialty vehicles with anti-corrosive engine parts were sold to accommodate fuel with higher concentrations of ethanol, including E85, a fuel mixture containing 85 percent ethanol. However, such vehicles and fuel types have yet to gain mass popularity.

13 The demand curve would likely not be fully vertical, as decreases in any fuel component's price, like ethanol's, would increase the quantity demanded of fuel. However, because ethanol makes up a small percentage of fuel, the demand curve is assumed to be nearly vertical.

14 U.S. Energy Information Administration (2020). See footnote 8.

© 2021, Federal Reserve Bank of St. Louis. The views expressed are those of the author(s) and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis or the Federal Reserve System.

Biology: The study of living organisms.

Chemistry: The branch of science that deals with the identification of the substances of which matter is composed.

Competitive markets: Markets in which there are generally many buyers and many sellers so that each has a negligible impact on market prices.

Demand: The quantity of a good or service that buyers are willing and able to buy at all possible prices during a certain time period.

Equilibrium price: The price at which quantity supplied and quantity demanded are equal. The point at which the supply and demand curves intersect.

Meteorology: The branch of science concerned with the processes and phenomena of the atmosphere, especially as a means of forecasting the weather.

Subsidies: Payments made by the government to support businesses or markets. No goods or services are provided in return for the payments.

Supply: The quantity of a good or service that producers are willing and able to sell at all possible prices during a certain time period.

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Article Contents

I. introduction, ii. literature, iii. supply shocks, iv. demand shock, v. combining supply and demand shocks, vi. conclusion.

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Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective

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R Maria del Rio-Chanona, Penny Mealy, Anton Pichler, François Lafond, J Doyne Farmer, Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective, Oxford Review of Economic Policy , Volume 36, Issue Supplement_1, 2020, Pages S94–S137, https://doi.org/10.1093/oxrep/graa033

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We provide quantitative predictions of first-order supply and demand shocks for the US economy associated with the COVID-19 pandemic at the level of individual occupations and industries. To analyse the supply shock, we classify industries as essential or non-essential and construct a Remote Labour Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 20 per cent of the US economy’s GDP, jeopardize 23 per cent of jobs, and reduce total wage income by 16 per cent. At the industry level, sectors such as transport are likely to be output-constrained by demand shocks, while sectors relating to manufacturing, mining, and services are more likely to be constrained by supply shocks. Entertainment, restaurants, and tourism face large supply and demand shocks. At the occupation level, we show that high-wage occupations are relatively immune from adverse supply- and demand-side shocks, while low-wage occupations are much more vulnerable. We should emphasize that our results are only first-order shocks—we expect them to be substantially amplified by feedback effects in the production network.

The COVID-19 pandemic is having an unprecedented impact on societies around the world. 1 As governments mandate social distancing practices and instruct non-essential businesses to close to slow the spread of the outbreak, there is significant uncertainty about the effect such measures will have on lives and livelihoods. While demand for specific sectors such as grocery stores increased in the early weeks of the pandemic, other sectors such as air transportation and tourism have seen demand for their services evaporate. At the same time, many sectors are experiencing issues on the supply side, as governments curtail the activities of non-essential industries and workers are confined to their homes.

In this paper, we aim to provide analytical clarity about the supply and demand shocks caused by public health measures and changes in preferences caused by avoidance of infection. We estimate (i) supply-side reductions due to the closure of non-essential industries and workers not being able to perform their activities at home, and (ii) demand-side changes due to peoples’ immediate response to the pandemic, such as reduced demand for goods or services that are likely to place people at risk of infection (e.g. tourism).

Several researchers have already provided estimates of the supply shock from labour supply ( Dingel and Neiman, 2020 ; Hicks et al. , 2020 ; Koren and Petö, 2020 ). Here we improve on these efforts in three ways: (i) we propose a methodology for estimating how much work can be done from home, based on work activities, (ii) we identify industries for which working from home is irrelevant because the industries are considered essential, and (iii) we compare our estimated supply shocks to estimates of the demand shock, which in many industries is the more relevant constraint on output.

A number of papers have also emphasized the importance of demand-side factors in the pandemic. For example, an early paper by Guerrieri et al. (2020) showed that, in a two-sector new Keynesian model with low substitutability in consumption, asymmetric labour supply shocks can lead to reductions in demand that are higher than the initial shock. In their framework, sectoral heterogeneity is necessary for supply shocks to lead to a larger demand impact. The scenario of a drop in demand larger than the drop in supply is also plausible if long-term labour supply constraints lead to a collapse of investment. This discussion underscores that supply and demand interact, and supply shocks can lead to decreases in demand. But as we argue here, the COVID pandemic also created exogenous and instantaneous changes to consumer demand, both in magnitude and in composition as consumers’ preferences change in response to factors such as infection risk, lower positive externalities in social consumption, explicit guidelines from the government, etc.

To see why it is important to compare supply and demand shocks, consider the following thought experiment. Following social-distancing measures, suppose industry i is capable of producing only 70 per cent of its pre-crisis output, e.g. because workers can produce only 70 per cent of the output while working from home. If consumers reduce their demand by 90 per cent, the industry will produce only what will be bought, that is, 10 per cent. If instead consumers reduce their demand by 20 per cent, the industry will not be able to satisfy demand but will produce everything it can, that is, 70 per cent. In other words, the experienced first-order reduction in output from the immediate shock will be the greater of the supply and the demand shock.

It is important to stress that the shocks that we predict here should not be interpreted as the overall impact of the COVID-19 pandemic on the economy. Again, we expect that as wages from work drop, there will be potentially larger second-order negative impacts on demand, and the potential for a self-reinforcing downward spiral in output, employment, income, and demand. Deriving overall impact estimates involves modelling second-order effects, such as the additional reductions in demand as workers who are stood down or laid off experience a reduction in income, and additional reductions in supply as potential shortages propagate through supply chains. Further effects, such as cascading firm defaults, which can trigger bank failures and systemic risk in the financial system, could also arise. Understanding these impacts requires a model of the macro-economy and financial sector. In a companion paper ( Pichler et al. , 2020 ), we present results from such an economic model, but we make our estimates of first-order impacts available separately here, for other researchers or governments to build upon or use in their own models.

Overall, we find that the supply and demand shocks considered in this paper represent a reduction of around one-fifth of the US economy’s value added, one-quarter of current employment, and about 16 per cent of the US total wage income. 2 Supply shocks account for the majority of this reduction. These effects vary substantially across different industries. While we find no negative effects on value added for industries like Legal Services, Power Generation and Distribution, or Scientific Research, the expected loss of value added reaches up to 80 per cent for Accommodation, Food Services, and Independent Artists.

We find that sectors such as Transport are likely to experience immediate demand-side reductions that are larger than their corresponding supply-side shocks. Other industries such as Manufacturing, Mining, and certain service sectors are likely to experience larger immediate supply-side shocks relative to demand-side shocks. Entertainment, restaurants, and hotels experience very large supply and demand shocks, with the demand shock dominating. These results are important because supply and demand shocks might have different degrees of persistence, and industries will react differently to policies depending on the constraints that they face. Overall, however, we find that aggregate effects are dominated by supply shocks, with a large part of manufacturing and services being classified as non-essential while its labour force is unable to work from home.

We also break down our results by occupation and show that there is a strong negative relationship between the overall immediate shock experienced by an occupation and its wage. Relative to the pre-COVID period, 41 per cent of the jobs for workers in the bottom quartile of the wage distribution are predicted to be vulnerable. (And bear in mind that this is only a first-order shock—second-order shocks may significantly increase this.) In contrast, most high-wage occupations are relatively immune from adverse shocks, with only 6 per cent of the jobs at risk for the 25 per cent of workers working in the highest pay occupations. Absent strong support from governments, most of the economic burden of the pandemic will fall on lower wage workers.

We neglect several effects that, while important, are small compared to those we consider here. First, we have not sought to quantify the reduction in labour supply due to workers contracting COVID-19. A rough estimate suggests that this effect is relatively minor in comparison to the shocks associated with social-distancing measures that are being taken in most developed countries. 3 We have also not explicitly included the effect of school closures. However, in   Appendix D2 . we argue that this is not the largest effect and is already partially included in our estimates through indirect channels.

A more serious problem is caused by the need to assume that within a given occupation, being unable to perform some work activities does not harm the performance of other work activities. Within an industry, we also assume that if workers in a given occupation cannot work, they do not produce output, but this does not prevent other workers in different occupations from producing. In both cases we assume that the effects of labour on production are linear, i.e. that production is proportional to the fraction of workers who can work. In reality, however, it is clear that there are important complementarities leading to nonlinear effects. There are many situations where production requires a combination of different occupations, such that if workers in key occupations cannot work at home, production is not possible. For example, while the accountants in a steel plant might be able to work from home, if the steelworkers needed to run the plant cannot come to work, no steel is made. We cannot avoid making linear assumptions because, as far as we know, there is no detailed understanding of the labour production function and these interdependencies at an industry level. By neglecting nonlinear effects, our work here should consequently be regarded as an approximate lower bound on the size of the first-order shocks.

This paper focuses on the United States. We have chosen it as our initial test case because input–output tables are more disaggregated than those of most other countries, and because the Occupational Information Network (O*NET) database, which we rely on for information about occupations, was developed based on US data. With some additional assumptions it is possible to apply the analysis we perform here to other developed countries.

This paper is structured as follows. In section II we review the most relevant literature on the economic impact of the pandemic and the associated supply and demand shocks. In section III we describe our methodology for estimating supply shocks, which involves developing a new Remote Labour Index (RLI) for occupations and combining it with a list of essential industries. Section IV discusses likely demand shocks based on estimates developed by the US Congressional Budget Office (2006) to predict the potential economic effects of an influenza pandemic. In section V, we show a comparison of the supply and demand shocks across different industries and occupations and identify the extent to which different activities are likely to be constrained by supply or demand. In this section, we also explore which occupations are more exposed to infection and make comparisons to wage and occupation-specific shocks. Finally, in section VI we discuss our findings in light of existing research and outline avenues for future work. We also make all of our data available in a continuously updated online repository ( https://zenodo.org/record/3744959 ).

Many economists and commentators believe that the economic impact could be dramatic ( Baldwin and Weder di Mauro, 2020 ). To give an example based on survey data in an economy under lockdown, the French statistical office estimated on 26 March 2020 that the economy is currently at around 65 per cent of its normal level. 4 Bullard (2020) provides an undocumented estimate that around a half of the US economy would be considered either essential, or able to operate without creating risks of diffusing the virus. Inoue and Todo (2020) modelled how shutting down firms in Tokyo would cause a loss of output in other parts of the economy through supply chain linkages, and estimate that after a month, daily output would be 86 per cent lower than pre-shock (i.e. the economy would be operating at only 14 per cent of its capacity!). Using a calibrated extended consumption function, and assuming a labour income shock of 16 per cent and various consumption shocks by expenditure categories, Muellbauer (2020) estimates a fall of quarterly consumption of 20 per cent. Roughly speaking, most of these estimates, like ours, are estimates of instantaneous declines, and would translate to losses of annual GDP if the lockdown lasted for a year.

Based on aggregating industry-level shocks, the OECD (2020) estimates a drop in immediate GDP of around 25 per cent. Another study by Barrot et al. (2020) estimates industry-level shocks by considering the list of essential industries, the closure of schools, and an estimate of the ability to work from home (based on ICT use surveys). Using these shocks in a multisector input–output model, they find that 6 weeks of social distancing would bring annual GDP down by 5.6 per cent.

Our study predicts supply and demand shocks at a disaggregated level, and proposes a simple method to calculate aggregate shocks from these. We take a short-term approach, and assume that the immediate drop in output is driven by the most binding constraint—the worse of the supply and demand shock, essentially assuming that prices do not adjust and markets do not clear. An alternative, standard in empirical macroeconomics, is to observe aggregate changes in prices and quantities to infer the relative size of the supply and demand shocks. For instance, Brinca et al. (2020) use data on wage and hours worked; Balleer et al. (2020) use data on planned price changes in German firms; and Bekaert et al. (2020) use surveys of inflation forecasts. While these studies do not agree on the relative importance of supply and demand shocks, an emerging consensus is that both supply and demand shocks co-exist and are vastly different across sectors, and over time.

Supply shocks from pandemics are mostly thought of as labour supply shocks. Several pre-COVID-19 studies focused on the direct loss of labour from death and sickness (e.g. McKibbin and Sidorenko (2006) , Santos et al. (2013) ), although some have also noted the potentially large impact of school closure ( Keogh-Brown et al. , 2010 ). McKibbin and Fernando (2020) consider (among other shocks) reduced labour supply due to mortality, morbidity due to infection, and morbidity due to the need to care for affected family members. In countries where social distancing measures are in place, such measures will have a much larger economic effect than the direct effects from mortality and morbidity. This is in part because if social distancing measures work, only a small share of population will be infected and die eventually.   Appendix D1 . provides more quantitative estimates of the direct mortality and morbidity effects and argues that they are likely to be at least an order of magnitude smaller than those due to social-distancing measures, especially if the pandemic is contained.

For convenience we neglect mortality and morbidity and assume that the supply shocks are determined only by the amount of labour that is withdrawn due to social distancing. We consider two key factors: (i) the extent to which workers in given occupations can perform their requisite activities at home, and (ii) the extent to which workers are likely to be unable to come to work due to being in non-essential industries. We quantify these effects on both industries and occupations. Figure 1 gives a schematic overview of how we predict industry and occupation specific supply shocks. We explain this in qualitative terms in the next few pages; for a formal mathematical description see   Appendix A.1 .

A schematic network representation of supply-side shocks

Notes : The nodes to the left represent the list of essential industries at the NAICS 6-digit level. A green node indicates essential, a red node non-essential. The orange nodes (centre-left) are more aggregate industry categories (e.g. 4-dig. NAICS or the BLS industry categories) for which further economic data are available. These two sets of nodes are connected through industry concordance tables. The blue nodes (centre-right) are different occupations. A weighted link connecting an industry category with an occupation represents the number of people of a given occupation employed in each industry. Nodes on the very right are O*NET work activities. Green work activities mean that they can be performed from home, while red means that they cannot. O*NET provides a mapping of work activities to occupations.

(i) How much work can be performed from home?

One way to assess the degree to which workers are able to work from home during the COVID-19 pandemic is by direct survey. For example, Zhang et al. (2020) conducted a survey of Chinese citizens in late February (1 month into the coronavirus-induced lockdown in China) and found that 27 per cent of the labour force continued working at the office, 38 per cent worked from home, and 25 per cent stopped working. Adams-Prassl et al. (2020) surveyed US and UK citizens in late March, and reported that the share of tasks that can be performed from home varies widely between occupations (from around 20 to 70 per cent), and that higher wage occupations tend to be more able to work from home.

Other recent work has instead drawn on occupation-level data from O*NET to determine labour shocks due to the COVID-19 pandemic. For example, Hicks et al. (2020) drew on O*NET’s occupational Work Context Questionnaire and considered the degree to which an occupation is required to ‘work with others’ or involves ‘physical proximity to others’ in order to assess which occupations are likely to be most impacted by social distancing. Dingel and Neiman (2020) aimed to quantify the number of jobs that could be performed at home by analysing responses on O*NET’s Work Context Questionnaire (such as whether the average respondent for an occupation spends the majority of time walking or running or uses email less than once per month) as well as responses on O*NET’s Generalized Work Activities Questionnaire (such as whether performing general physical activities or handling and moving objects is very important for a given occupation).

In this study, we go to a more granular level than both the Work Context Questionnaire and Generalized Work Activities Questionnaire, and instead draw on O*NET’s ‘intermediate work activity’ data, which provide a list of the activities performed by each occupation based on a list of 332 possible work activities. For example, a nurse undertakes activities such as ‘maintain health or medical records’, ‘develop patient or client care or treatment plans’, and ‘operate medical equipment’, while a computer programmer performs activities such as ‘resolve computer programs’, ‘program computer systems or production equipment’, and ‘document technical designs, producers or activities’. 5 In Figure 1 these work activities are illustrated by the rightmost set of nodes.

Which work activities can be performed from home?

Four of us independently assigned a subjective binary rating to each work activity as to whether it could successfully be performed at home. The individual results were in broad agreement. Based on the responses, we assigned an overall consensus rating to each work activity. 6 Ratings for each work activity are available in an online data repository. 7 While O*NET maps each intermediate work activity to 6-digit O*NET occupation codes, employment information from the US Bureau of Labor Statistics (BLS) is available for the 4-digit 2010 Standard Occupation Scheme (SOC) codes, so we mapped O*NET and SOC codes using a crosswalk available from O*NET. 8 Our final sample contains 740 occupations.

From work activities to occupations.

We then created a Remote Labour Index (RLI) for each occupation by calculating the proportion of an occupation’s work activities that can be performed at home. An RLI of 1 would indicate that all of the activities associated with an occupation could be undertaken at home, while an RLI of 0 would indicate that none of the occupation’s activities could be performed at home. 9 The resulting ranking of each of the 740 occupations can be found in the online repository (see footnote 6). In contrast to previous work that has tended to arrive at binary assessments of whether an occupation can be performed at home, our approach has the advantage of providing a unique indication of the amount of work performed by a given occupation that can be done remotely. While the results are not perfect, 10 most of the rankings make sense. For example, in Table 1 , we show the top 20 occupations having the highest RLI ranking. Some occupations such as credit analysts, tax preparers, and mathematical technician occupations are estimated to be able to perform 100 per cent of their work activities from home. Table 1 also shows a sample of the 43 occupations with an RLI ranking of zero, i.e. those for which there are no activities that can be performed at home.

Top and bottom 20 occupations ranked by Remote Labour Index (RLI), based on proportion of work activities that can be performed at home

Note : There are 44 occupations with an RLI of zero; we show only a random sample.

To provide a broader perspective of how the RLI differs across occupation categories, Figure 2 shows a series of box-plots indicating the distribution of RLI for each 4-digit occupation in each 2-digit SOC occupation category. We have ordered 2-digit SOC occupations in accordance with their median values. Occupations with the highest RLI relate to Education, Training and Library, Computer and Mathematical, and Business and Financial roles, while occupations relating to Production, Farming, Fishing and Forestry, and Construction and Extraction tend to have lower RLI.

Distribution of RLI across occupations

Note : We provide boxplots showing distribution of RLI for each 4-digit occupation in each 2-digit SOC occupation category.

From occupations to industries.

We next map the RLI to industry categories to quantify industry-specific supply shocks from social distancing measures. We obtain occupational compositions per industry from the BLS, which allows us to match 740 occupations to 277 industries. 11

In Figure 3 , we show the RLI distribution for each 4-digit occupation category falling within each broad 2-digit NAICS category. Similar to Figure 2 , we have ordered the 2-digit NAICS industry categories in accordance with the median values of each underpinning distribution. As there is a greater variety of different types of occupations within these broader industry categories, distributions tend to be much wider. Industries with the highest median RLI values relate to Information, Finance and Insurance, and Professional, Science and Technical Services, while industries with the lowest median RLI relate to Agriculture, Forestry, Fishing and Hunting and Accommodation and Food Services.

Distribution of RLI across industries

Note : We provide boxplots showing distribution of RLI for each 4-digit occupation in each 2-digit NAICS Industry category.

In   Appendix B , we show industry-specific RLI values for the more detailed 4-digit NAICS industries. To arrive at a single number for each 4-digit industry, we compute the employment-weighted average of occupation-specific RLIs. The resulting industry-specific RLI can be interpreted as a rough estimate of the fraction of jobs which can be performed from home for each industry.

(ii) Which industries are ‘essential’?

Across the world, many governments have mandated that certain industries deemed ‘essential’ should remain open over the COVID-19 crisis duration. What constitutes an ‘essential’ industry has been the subject of significant debate, and it is likely that the endorsed set of essential industries will vary across countries. As the US government has not produced a definitive list, here we draw on the list of essential industries developed by Italy and assume it can be applied, at least as an approximation, to other countries, such as the US, as well. This list has two key advantages. First, as Italy was one of the countries affected earliest and most severely, it was one of the first countries to invest significant effort considering which industries should be deemed essential. Second, Italy’s list of essential industries includes NACE industrial classification codes, which can be mapped to the NAICS industry classification we use to classify industrial employment in this paper. 12

Table 2 shows the total numbers of NAICS essential industries at the 6-digit and 4-digit level. More than 50 per cent of 6-digit NAICS industries are considered essential. At the 6-digit level the industries are either classified as essential, and assigned essential score 1, or non-essential and assigned essential score 0. Unfortunately, it is not possible to translate this directly into a labour force proportion as BLS employment data at detailed occupation and industry levels are only available at the NAICS 4-digit level. To derive an estimate at the 4-digit level, we assume that labour in a NAICS 4-digit code is uniformly distributed over its associated 6-digit codes. We then assign an essential ‘share’ to each 4-digit NAICS industry based on the proportion of its 6-digit NAICS industries that are considered essential. (The distribution of the essential share over 4-digit NAICS industries is shown in   Appendix B .) Based on this analysis, we estimate that about 92m (or 67 per cent) of US workers are currently employed in essential industries.

Essential industries

Notes : Essential industries at the 6-digit level and essential ‘share’ at the 4-digit level. Note that 6-digit NAICS industry classifications are binary (0 or 1) whereas 4-digit NAICS industry classifications can take on any value between 0 and 1.

(iii) Supply shock: non-essential industries unable to work from home

Having analysed both the extent to which jobs in each industry are essential and the likelihood that workers in a given occupation can perform their requisite activities at home, we now combine these to consider the overall first-order effect on labour supply in the US. In Figure 4 , we plot the RLI of each occupation against the fraction of that occupation employed in an essential industry. Each circle in the scatter plot represents an occupation; the circles are sized proportional to current employment and colour coded according to the median wage in each occupation.

Fraction employed in an essential industry vs Remote Labour Index for each occupation

Notes : Omitting the effect of demand reduction, the occupations in the lower left corner, with a small proportion of workers in essential industries and a low Remote Labour Index, are the most vulnerable to loss of employment due to social distancing.

Figure 4 indicates the vulnerability of occupations due to supply-side shocks. Occupations in the lower left-hand side of the plot (such as Dishwashers, Rock Splitters, and Logging Equipment Operators) have lower RLI scores (indicating they are less able to work from home) and are less likely to be employed in an essential industry. If we consider only the immediate supply-side effects of social distancing, workers in these occupations are more likely to face reduced work hours or be at risk of losing their jobs altogether. In contrast, occupations on the upper right-hand side of the plot (such as Credit Analysis, Political Scientists, and Operations Research Analysts) have higher RLI scores and are more likely to be employed in an essential industry. These occupations are less economically vulnerable to the supply-side shocks (though, as we discuss in the next section, they could still face employment risks due to first-order demand-side effects). Occupations in the upper-left hand side of the plot (such as Farmworkers, Healthcare Support Workers, and Respiratory Therapists) are less likely to be able to perform their job at home, but since they are more likely to be employed in an essential industry their economic vulnerability from supply-side shocks is lower. Interestingly, there are relatively few occupations on the lower-right hand side of the plot. This indicates that occupations that are predominantly employed in non-essential industries tend to be less able to perform their activities at home.

To help visualize the aggregate numbers we provide a summary in the form of a Venn diagram in Figure 5 . Before the pandemic, 33 per cent of workers were employed in non-essential jobs. 56 per cent of workers are estimated to be unable to perform their job remotely. 19 per cent of workers are in the intersection corresponding to non-essential jobs that cannot be performed remotely. In addition, there are 30 per cent of workers in essential industries that can also work from home. 13

Workers that cannot work

Notes : On the left is the percentage of workers in a non-essential job (33 per cent in total). On the right is the percentage of workers that cannot work remotely (56 per cent in total). The intersection is the set of workers that cannot work, which is 19 per cent of all workers. A remaining 30 per cent of workers are in essential jobs where they can work remotely.

The pre-COVID-19 literature on epidemics and the discussions of the current crisis make it clear that epidemics strongly influence patterns of consumer spending. Consumers are likely to seek to reduce their risk of exposure to the virus and decrease demand for products and services that involve close contact with others. In the early days of the outbreak, stockpiling behaviour also drives a direct demand increase in the retail sector ( Baker et al. , 2020 ).

Estimates from the CBO

Our estimates of the demand shock are based on expert estimates developed by the US Congressional Budget Office (2006) that attempted to predict the potential impact of an influenza pandemic. Similar to the current COVID-19 pandemic, this analysis assumes that demand is reduced due to the desire to avoid infection. While the analysis is highly relevant to the present COVID-19 situation, it is important to note that the estimates are ‘extremely rough’ and ‘based loosely on Hong Kong’s experience with SARS’. The CBO provides estimates for two scenarios (mild and severe). We draw on the severe scenario, which

describes a pandemic that is similar to the 1918–1919 Spanish flu outbreak. It incorporates the assumption that a particularly virulent strain of influenza infects roughly 90 million people in the United States and kills more than 2 million of them.

In this paper, we simply take the CBO estimates as immediate (first-order) demand-side shocks. The CBO lists demand-side estimates for broad industry categories, which we mapped to the 2-digit NAICS codes by hand. Table 3 shows the CBO’s estimates of the percent decrease in demand by industry, and Table 8 in   Appendix E shows the full mapping to 2-digit NAICS.

Demand shock by sector according to the Congressional Budget Office (2006) ’s severe scenario

These estimates, of course, are far from perfect. They are based on expert estimates made more than 10 years ago for a hypothetical pandemic scenario. It is not entirely clear if they are for gross output or for final (consumer) demand. However, in   Appendix E , we describe three other sources of consumption shocks ( Keogh-Brown et al. , 2010 ; Muellbauer, 2020 ; OECD, 2020 ) that provide broadly similar estimates by industry or spending category. We also review papers that have appeared more recently and contained estimates of consumption changes based on transaction data. Taken together, these papers suggest that the shocks from the CBO were qualitatively accurate: very large declines in the hospitality, entertainment, and transport industries, milder declines in manufacturing, and a more resistant business services sector. The main features that have been missed are the increase in demand, at least early on, in some specific retail categories (groceries), and the decline in health consumption, in sharp contrast with the CBO prediction of a 15 per cent increase.

Aggregate consumption vs composition of the shocks

The shocks from the CBO include two separate effects: a shift of preferences, where the utility of healthcare relative to restaurants, say, increases; and an aggregate consumption effect. Here, we do not go further in distinguishing these effects, although this becomes necessary in a more fully-fledged model ( Pichler et al. , 2020 ). Yet, it remains instructive to note that, in Muellbauer’s (2020) consumption function estimates, the decline in aggregate consumption is not only due to direct changes in consumption in specific sectors, but also to lower income, rising income insecurity (due to unemployment in particular), and wealth effects (due in particular to falling asset prices).

Transitory and permanent shocks

An important question is whether demand reductions are just postponed expenses, and if they are permanent ( Keogh-Brown et al. , 2010 ; Mann, 2020 ). Baldwin and Weder di Mauro (2020) also distinguish between ‘practical’ (the impossibility to shop) and ‘psychological’ (the wait-and-see attitude adopted by consumers facing strong uncertainty) demand shocks. We see three possibilities: (i) expenses in a specific good or service are just delayed but will take place later, for instance if households do not go to the restaurant this quarter, but go twice as often as they would normally during the next quarter; (ii) expenses are not incurred this quarter, but will come back to their normal level after the crisis, meaning that restaurants will have a one-quarter loss of sales; and (iii) expenses decrease to a permanently lower level, as household change their preferences in view of the ‘new normal’.   Appendix E reproduces the scenario adopted by Keogh-Brown et al. (2010) , which distinguishes between delay and permanently lost expenses.

Other components of aggregate demand

We do not include direct shocks to investment, net exports, and net inventories. Investment is typically very pro-cyclical and is likely to be strongly affected, with direct factors including cash-flow reductions and high uncertainty ( Boone, 2020 ). The impact on trade is likely to be strong and possibly permanent ( Baldwin and Weder di Mauro, 2020 ), but would affect exports and imports in a relatively similar way, so the overall effect on net exports is unclear. Finally, it is likely that due to the disruption of supply chains, inventories will be run down so the change in inventories will be negative ( Boone, 2020 ).

Having described both supply- and demand-side shocks, we now compare the two at the industry and occupation level.

(i) Industry-level supply and demand shocks

Figure 6 plots the demand shock against the supply shock for each industry. The radius of the circles is proportional to the gross output of the industry. 14 Essential industries have no supply shock and so lie on the horizontal ‘0’ line. Of these industries, sectors such as Utilities and Government experience no demand shock either, since immediate demand for their output is assumed to remain the same. Following the CBO predictions, Health experiences an increase in demand and consequently lies below the identity line. Transport, on the other hand, experiences a reduction in demand and lies well above the identity line. This reflects the current situation, where trains and buses are running because they are deemed essential, but they are mostly empty. Non-essential industries such as Entertainment, Restaurants, and Hotels, experience both a demand reduction (due to consumers seeking to avoid infection) and a supply reduction (as many workers are unable to perform their activities at home). Since the demand shock is bigger than the supply shock, they lie above the identity line. Other non-essential industries, such as Manufacturing, Mining, and Retail, have supply shocks that are larger than their demand shocks and consequently lie below the identity line.

Supply and demand shocks for industries

Notes : Each circle is an industry, with radius proportional to gross output. Many industries experience exactly the same shock, hence the superposition of some of the circles. Labels correspond to broad classifications of industries.

(ii) Occupation-level supply and demand shocks

In Figure 7 we show the supply and demand shocks for occupations rather than industries. For each occupation this comparison indicates whether it faces a risk of unemployment due the lack of demand or a lack of supply in its industry.

Supply and demand shocks for occupations

Notes : Each circle is an occupation with radius proportional to employment. Circles are colour coded by the log median wage of the occupation. The correlation between median wages and demand shocks is 0.26 (p-value = 2.8 × 10 –13 ) and between median wages and supply shocks is 0.41 (p-value = 1.5 × 10 –30 ).

Several health-related occupations, such as Nurses, Medical Equipment Preparers, and Healthcare Social Workers, are employed in industries experiencing increased demand. Occupations such as Airline Pilots, Lodging Managers, and Hotel Desk Clerks face relatively mild supply shocks and strong demand shocks (as consumers reduce their demand for travel and hotel accommodation) and consequently lie above the identity line. Other occupations such as Stonemasons, Rock Splitters, Roofers, and Floor Layers face a much stronger supply shock as it is very difficult for these workers to perform their job at home. Finally, occupations such as Cooks, Dishwashers, and Waiters suffer both adverse demand shocks (since demand for restaurants is reduced) and supply shocks (since they cannot work from home and tend not to work in essential industries).

For the majority of occupations, the supply shock is larger than the demand shock. This is not surprising given that we only consider immediate shocks and no feedback-loops in the economy. We expect that once second-order effects are considered the demand shocks are likely to be much larger.

(iii) Aggregate shocks

We now aggregate shocks to obtain estimates for the whole economy. We assume that, in a given industry, the total shock will be the largest of the supply or demand shocks. For example, if an industry faces a 30 per cent demand shock and 50 per cent supply shock because 50 per cent of the industry’s workforce cannot work, the industry is assumed to experience an overall 50 per cent shock to output. For simplicity, we assume a linear relationship between output and labour: i.e. when industries are supply constrained, output is reduced by the same fraction as the reduction in labour supply. This assumption also implies that the demand shock that workers of an industry experience equals the industry’s output demand shock in percentage terms. For example, if transport faces a 67 per cent demand shock and no supply shock, bus drivers working in this industry will experience an overall 67 per cent employment shock. The shock on occupations depends on the prevalence of each occupation in each industry (see   Appendix A for details). We then aggregate shocks in three different ways.

First, we estimate the decline in employment by weighting occupation-level shocks by the number of workers in each occupation. Second, we estimate the decline in total wages paid by weighting occupation-level shocks by the share of occupations in the total wage bill. Finally, we estimate the decline in GDP by weighting industry-level shocks by the share of industries in GDP. 15

Table 4 shows the results. In all cases, by definition, the total shock is larger than both the supply and demand shock, but smaller than the sum. Overall, the supply shock appears to contribute more to the total shock than does the demand shock.

Aggregate shocks to employment, wages, and value added

Notes : The size of each shock is shown as a percentage of the pre-pandemic value. Demand shocks include positive values for the health sector. The total shock at the industry level is the minimum of the supply and demand shock, see   Appendix A . Note that these are only first-order shocks (not total impact), and instantaneous values (not annualized).

The wage shock is around 16 per cent and is lower than the employment shock (23 per cent). This makes sense, and reflects a fact already well acknowledged in the literature ( Office for National Statistics, 2020 ; Adams-Prassl et al. , 2020 ) that occupations that are most affected tend to have lower wages. We discuss this more below.

For industries and occupations in the health sector, which experience an increase in demand in our predictions, there is no corresponding increase in supply. Table 6 in   Appendix A.7 provides the same estimates as Table 4 , but now assuming that the increased demand for health will be matched by increased supply. This corresponds to a scenario where the healthcare sector would be immediately able to hire as many workers as necessary and pay them at the normal rate. This assumption does not, however, make a significant difference to the aggregate total shock. In other words, the increase in activity in the health sector is unlikely to be large enough to compensate significantly for the losses from other sectors.

(iv) Shocks by wage level

To understand how the pandemic has affected workers of different income levels differently, we present results for each wage quartile. The results are in Table 5 , columns q 1 ... q 4, 16 where we show employment shocks by wage quartile. This table shows that workers whose wages are in the lowest quartile (lowest 25 per cent) will bear much higher relative losses than workers whose wages are in the highest quartile. Our results confirm the survey evidence reported by the Office for National Statistics (2020) and Adams-Prassl et al. (2020) , showing that low-wage workers are more strongly affected by the COVID crisis in terms of lost employment and lost income. Furthermore, Table 5 shows how the total loss of wages in the economy is split amongst the different quartiles. Even though those in the lowest quartile have lower salary, the shock is so high that they bear the highest share of the total loss.

Total wages or employment shocks by wage quartile

Notes : We divide workers into wage quartiles based on the average wage of their occupation ( q 1 corresponds to the 25 per cent least-paid workers). The first row is the number of workers who are vulnerable due to the shock in each quartile divided by the total who are vulnerable. Similarly, the second row is the fraction of whole economy total wages loss that would be lost by vulnerable workers in each quartile. The last column gives the aggregate shocks from Table 4 .

Next we estimate labour shocks at the occupation level. We define the labour shocks as the declines in employment due to the total shocks in the industries associated with each occupation. We use Eq. (14) (  Appendix A.7 ) to compute the labour shocks, which allows for positive shocks in healthcare workers, to suggest an interpretation in terms of a change in labour demand. Figure 8 plots the relationship between labour shocks and median wage. A strong positive correlation (Pearson ρ = 0.40, p-value = 3.5 × 10 –30 ) is clearly evident, with almost no high-wage occupations facing a serious shock.

Labour shock vs median wage for different occupations

Notes : We colour occupations by their exposure to disease and infection. There is a 0.40 correlation between wages and the labour shock (p-value = 3.5 × 10 –30 ). Note the striking lack of high-wage occupations with large labour demand shocks.

We have also coloured occupations by their exposure to disease and infection using an index developed by O*NET 17 (for brevity we refer to this index as ‘exposure to infection’). As most occupations facing a positive labour shock relate to healthcare, 18 it is not surprising to see that they have a much higher risk of being exposed to disease and infection. However, other occupations such as janitors, cleaners, maids, and childcare workers also face higher risk of infection.   Appendix C explores the relationship between exposure to infection and wage in more detail.

This paper has sought to provide quantitative predictions for the US economy of the supply and demand shocks associated with the COVID-19 pandemic. To characterize supply shocks, we developed a Remote Labour Index (RLI) to estimate the extent to which workers can perform activities associated with their occupation at home and identified which industries are classified as essential vs non-essential. We also reported plausible estimates of the demand shocks, in an attempt to acknowledge that some industries will have an immediate reduction in output due to a shortfall in demand, rather than due to an impossibility to work. We would like to emphasize that these are predictions , not measurements. The estimates of the demand shocks were made in 2006, and the RLI and the list of non-essential industries contain no pandemic-specific information, and could have been made at any time. Putting these predictions together, we estimate that the first-order aggregate shock to the economy represents a reduction of roughly a fifth of the economy.

This is the first study seeking to compare supply-side shocks with corresponding demand-side shocks at the occupation and industry level. At the time of writing (mid-April), the most relevant demand-side estimates available are admittedly highly ‘rough’ and only available for very aggregate (2-digit) industries. Yet, this suggests that sectors such as transport are more likely to have output constrained by demand-side shocks, while sectors relating to manufacturing, mining, and services are more likely to be constrained by supply-side shocks. Entertainment, restaurants, and tourism face both very large supply and demand constraints, with demand shocks dominating in our estimates. By quantifying supply and demand shocks by industry, our paper speaks to the debate on the possibility of inflation after the crisis. Goodhart and Pradhan (2020) argue that the lockdown causes a massive supply shock that will lead to inflation when demand comes back after the crisis. But as Miles and Scott (2020) note, in many sectors it is not obvious that demand will come back immediately after the crisis, and if a gradual reopening of the economy takes place, it may be that supply and demand rise slowly together. However, our paper is the first to raise the fact that because supply and demand shocks are so different by sectors, even a gradual reopening may leave important supply–demand imbalances within industries. Such mismatches could consequently lead to an unusual level of heterogeneity in the inflation for different goods.

When considering total shocks at the occupation level, we find that high-wage occupations are relatively immune from both supply- and demand-side shocks, while many low-wage occupations are much more economically vulnerable to both. Interestingly, low-wage occupations that are not vulnerable to supply- and/or demand-side shocks are nonetheless at higher risk of being exposed to coronavirus (see colour code in Figure 8 ). Such findings suggest that the COVID-19 pandemic is likely to exacerbate income inequality in what is already a highly unequal society.

For policy-makers there are three key implications from this study. First, the magnitude of the shocks being experienced by the US economy is very large, with around a fifth of the economy not functioning. As Table 4 shows, even including positive shocks, our estimates of the potential impacts are a drop in employment of 23 per cent, a decline in wages of 16 per cent, and loss in value added of 20 per cent. Bearing in mind the caveats about shocks vs total impacts, the potential impacts are a multiple of what was experienced during the Global Financial Crisis (e.g. where employment dropped 3.28 percentage points) 19 and comparable only to the Great Depression (e.g. where employment dropped 21.7 per cent 1929–32 ( Wallis, 1989 , Table 2) ). Second, as the largest shocks are from the supply side, strategies for returning people to work as quickly as possible without endangering public health must be a priority. Virus mitigation and containment are clearly essential first steps, but strategies such as widespread antibody testing to identify people who are safe to return to work, and rapid testing, tracing, and isolation to minimize future lock-downs, will also be vital until, if and when, a vaccine is available. Furthermore, aggressive fiscal and monetary policies to minimize first-order shocks cascading into second-order shocks are essential, in particular policies to keep workers in employment and maintain incomes (e.g. the ‘paycheck protection’ schemes announced by several countries), as well as policies to preserve business and financial solvency. Third, and finally, the inequalities highlighted by this study will also require policy responses. Again, higher-income knowledge and service workers will likely see relatively little impact, while lower-income workers will bear the brunt of the employment, income, and health impacts. In order to ensure that burdens from the crisis are shared as fairly as possible, assistance should be targeted at those most affected, while taxes to support such programmes should be drawn primarily from those least affected.

To reiterate an important point, our predictions of the shocks are not estimates of the overall impact of the COVID-19 on the economy, but are rather estimates of the first-order shocks. Overall impacts can be very different from first-order shocks for several reasons. First, shocks to a particular sector propagate and may be amplified as each industry faces a shock and reduces its demand for intermediate goods from other industries ( Pichler et al. , 2020 ). Second, industries with decreased output will stop paying wages of furloughed workers, thereby reducing income and demand; importantly, reduction in supply in capacity-constrained sectors lead to decreases in expenditures in other sectors, and the details of these imbalances will determine the overall impact ( Guerrieri et al. , 2020 ). Third, the few industries facing higher demand will increase supply, if they can overcome labour mobility frictions ( del Rio-Chanona et al. , 2019 ). Fourth, the final outcomes will very much depend on the policy response, and in particular the ability of government to maintain (consumption and investment) demand and limit the collapse of the labour market, in a context where the shocks are extremely heterogenous across industries, occupations, and income levels. We make our predictions of the shocks available here so that other researchers can improve upon them and use them in their own models. 20 We intend to update and use these shocks ourselves in our models in the near future.

We have made a number of strong assumptions and used data from different sources. To recapitulate, we assume that the production function for an industry is linear and that it does not depend on the composition of occupations who are still able to work; we neglect absenteeism due to mortality and morbidity, as well as loss of productivity due to school closures (though we have argued these effects are small—see   Appendix D.1 ). We have constructed our Remote Labour Index based on a subjective rating of work activities and we assumed that all work activities are equally important and they are additive. We have also applied a rating of essential industries for Italy to the US. Nonetheless, we believe that the analysis here provides a useful starting point for macroeconomic models attempting to measure the impact of the COVID-19 pandemic on the economy.

As new data become available we will be able to test whether our predictions are correct and improve our shock estimate across industries and occupations. Several countries have already started to release survey data. New measurements about the ability to perform work remotely in different occupations are also becoming available. New York and Pennsylvania have released a list of industries that are considered essential 21 (though this is not currently associated with any industrial classification such as NACE or NAICS). Germany and Spain have also released a list of essential industries ( Fana et al. , 2020 ). As new data become available for the mitigation measures different states and countries are taking, we can also refine our analysis to account for different government actions. Thus we hope that the usefulness of methodology we have presented here goes beyond the immediate application, and will provide a useful framework for predicting economic shocks as the pandemic develops.

We would like to thank Eric Beinhocker, Stefania Innocenti, John Muellbauer, Marco Pangallo, and David Vines for many comments and discussions. We are also grateful to Andrea Bacilieri and Luca Mungo for their help with the list of essential industries. We thank Baillie Gifford, IARPA, the Mexican Energy Ministry (SENER), the Mexican Science and Technology Research Council (CONACyT Mexico), and the Oxford Martin School for the funding that made this possible. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA-BAA-17-01, Contract No. 2019-19020100003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US government. The US government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.

A. Derivation of total shocks

A.1 derivation of supply shocks.

As discussed in the main text, we estimate the supply shock by computing an estimate of the share of work that will not be performed, which we compute by estimating the share of work that is not in an essential industry and that cannot be performed from home. We had to use several concordance tables, and make a number of assumptions, which we describe in detail here.

Figure 9 illustrates our method. There are four sets of nodes which are connected by three bipartite networks. The first set of nodes are the 6-digit NAICS industries which are classified to be essential or non-essential. This information is encoded in the K -dimensional column vector u which element u k = 1 if NAICS 6-digit industry k is essential and 0 otherwise. Second, there are N different industry categories on which our economic analysis is based. The 6-digit NAICS codes are connected to these industries by the incidence matrix (concordance table) S . The third set of nodes are the J occupations obtained from the BLS and O*NET data. The weighted incidence matrix M couples industries with occupations where the element M nj denotes the number of people in occupation j being employed in industry n . Fourth, we also have a list of I work activities. Each activity was rated whether it can be performed from home. If activity i can be done from home, the i th element of the vector v is equal to 1, and otherwise it is equal to 0. The incidence matrix T denotes whether an occupation is associated with any given work activity, i.e. T ji = 1 if activity i is relevant for occupation j .

The same schematic network representation of supply-side shocks as in the main text, but now also including mathematical notation

Notes : The K -dimensional vector u below the NAICS 6-dig. (left nodes) encodes essential industries with binary elements. This set of nodes is connected to relevant industry categories by concordance tables (incidence matrix S ). Matrix M connects the N industry categories with J occupations where an element represents the corresponding employment number. The ability to perform work activities (right nodes) from home is represented in vector v , also by binary elements. We use occupation-activity mappings provided from O*NET, represented as incidence matrix T . The grey arrows show the direction of shocks to industries and employment. The shock originating from the list of essential industries is mapped directly on to the broader industry categories, before it can be computed for occupations. Conversely, the Remote Labour Index is first mapped on to occupations and then projected on to industries.

The analysis presented here is based on I = 332 unique work activities, J = 740 occupations, and K = 1,057 6-digit NAICS industries. When relating to industry-specific results we use the BLS industry categories of the input–output accounts. We are able to derive supply shocks for N = 170 industries (out of 182 industries in the BLS data) for which we also have reliable data on value added, total output, and other key statistics. Employment, occupation, and wage statistics are available on a more fine-grained 4-digit NAICS level. We therefore use these N = 277 industries for deriving labour-specific results.

A.2 Industry-specific shocks

We can use this simple framework for deriving the supply shocks to industries.

(Non-)essential industries

To estimate the extent to which an industry category is affected by a shutdown of non-essential economic activities, we measure the fraction of its 6-digit NAICS sub-industries which are classified as non-essential. In mathematical terms, the essential-score for every industry is therefore a weighted sum which can be written compactly in matrix notation as

where S’ is the row-normalized version of matrix S with elements S’ nk = S nk / ∑  h S nh .

Note that this assumes that the fine-grained NAICS codes contribute uniformly to the more aggregate industry categories. Although this assumption might be violated in several cases, in absence of further information, we use this assumption throughout the text. Finally, we revised the essential score e of all industries. With the help of two colleagues with knowledge of the current situation in Italy we reclassified a small subset of industries with implausible essential scores (see   Appendix B for details).

Industry Remote Labour Index

We can similarly estimate the extent to which the production of occupations or industries can take place by working from home. Since work activities are linked to occupations, but not directly to industries, we need to take two weighted averages to obtain the industry-specific RLI.

For each occupation we first measure the fraction of work activities that can be done from home. We interpret this as the share of work of an occupation that can be performed from home, or ‘occupation-level RLI’. This interpretation makes two assumptions: (i) that every work activity contributes equally to an occupation, which is our best guess since we do not have better data, and (ii) that if z per cent of activities cannot be done from home, the other 1 – z per cent of activities can still be carried out and are as productive as before.

For each industry i we then take a weighted average of the occupation-level RLIs, where the weights are the shares of workers employed in each occupation and in industry i . Let T’ denote the row-normalized version of matrix T , i.e. T’ ji = T ji / ∑  h T jh and similarly let the element of matrix M’ be M’ nj = M nj / ∑  h M nh . Then the industry-specific RLI is given by the vector

We interpret the RLI for an industry, r n , as the fraction of work in an industry n that can be performed from home. As for assumption (ii) above for the occupation-level RLI, this assumes that if z per cent of the work of occupations cannot be done, the other 1 – z per cent of work can still be carried out.

Immediate industry supply shock

To derive industry supply shocks from the scores above, we need to take into account that industries might be exposed to both effects at the same time, but with different magnitudes. For example, consider the illustrative case of Chemical Manufacturing in Figure 9 . Half of the industry is non-essential (red node ‘325130’) and could therefore be directly affected by an economic shutdown. But different occupations can be found in this industry that are affected heterogeneously. In this simple example, Chemical Manufacturing draws heavily on Boilermakers who have only work activities that cannot be done from home. On the other hand, this industry also has a tiny share of accountants and a larger share of Chemical Engineers who are able to do half of their work activities from home.

As stated above, the essential score e n and the RLI r n can be interpreted as shares of industry-specific work which can be performed, either thanks to being essential or thanks to being adequately done from home. To compute the share of industry-specific work that can be performed due to either effect, we interpret shares as probabilities and assume independence,

where ISS stands for ‘industry supply shock’. We have multiplied the probability by minus one to obtain negative shocks. Although independence is a strong assumption, we have no reason to believe that the work that can be done from home is more or less likely to be judged essential. The empirical correlation coefficient of e and r is 0.04 and is far from being significant (p-value of 0.5), indicating that the independence assumption should have only minor effects on our results.

When applying these industry supply shocks to value added, we make the implicit assumption that a z per cent decrease in labour will cause a z per cent decrease in value added.

A.3 Occupation-specific shocks

We now describe how we compute shocks for specific occupations, rather than specific industries.

Occupations in (non-)essential industries

Occupations are mapped to industries through the weighted incidence matrix M , where an element denotes the number of jobs per occupation and industry.

The column-normalized matrix M ∗ with elements M n j ∗ =   M n j / ∑ h M h j denotes the share of an occupation carried out in a particular industry. 22 The essential-score for occupations is taken as weighted average of the essential score for industries (computed in Eq. 1),

Occupation Remote Labour Index

As already indicated in the derivation of the industry-specific RLI, r , in Eq. (2), the occupation-specific RLI, y , is a weighted average of all the corresponding work activities that can be done from home. Formally, the occupation-based RLI is given by

Total supply-driven occupation shock

Following the same procedure as in Eq. (3), we can get the total immediate shock on occupations from the economy’s supply side. 23 The combined immediate shock to occupations is then given as

Here, the correlation between RLI and the essential-score is larger, ρ( x, y ) = 0.32 (p-value =2.8 × 10 –19 ), and significant, which can also be seen from Figure 4 . It should therefore be noted that the labour-specific results are expected to be more sensitive with respect to the independence assumption, as is the case for industry-related results.

A.4 Derivation of demand shocks

Since we have demand shocks only on the 2-digit NAICS level, disaggregating them into the more fine-grained relevant industry categories is straightforward when assuming that the demand shock holds equally for all sub-industries. We let the industry demand shock in percentages for industry n be –IDS n .

To map the demand shocks on to occupations, we can invoke the same matrix algebra as above. The occupation-specific shock originating from the economy’s demand side is then given by the projection

A.5 Total immediate (first-order) shocks

We now combine supply- and demand-driven shocks to total immediate shocks for occupations and industries.

Let us turn to industries first. As discussed in more depth in the main text, the shock experienced in the very short term is likely to be the worse of the two (supply and demand) shocks. Since we have expressed shocks as negative if they lead to decrease in output, in more mathematical terms, the industry total shock then is

and the occupation total shock is

Under these assumptions, the health sector will not experience a positive shock. We provide an alternative treatment in   Appendix A.7 .

A.6 Aggregate total shocks

To provide an economy-wide estimate of the shocks, we aggregate industry- or occupation-level shocks. We do this using different sets of weights.

First of all, consider the interpretation that our shocks at the occupation level represent the share of work that will not be performed. If we assume that if z per cent of the work cannot be done, z per cent of the workers will become unemployed, we can weigh the occupation shocks by the share of employment in each occupation. Using the vector L to denote the share of employed workers that are employed by occupation j , we have

The employment supply (demand) shock is computed similarly but using OSS (ODS) instead of OTS.

Instead of computing how many workers may lose their jobs, we can compute by how much paid wages will decrease. For each occupation, we compute the total wage bill by multiplying the number of workers by the average wage. We then create a vector w where w j is the share of occupation j in the total wage bill. Then,

and similarly for the OSS and ODS. Note that we omit three occupations for which we do not have wages (but had employment).

Finally, to get an estimate of the loss of GDP, we can aggregate shocks by industry, weighting by the share of an industry in GDP. Denoting by Y the vector where Y n is the VA of industry n divided by GDP, 24

and similarly for the industry supply and demand shocks (ISS and IDS). Note that we could have used shares of gross output and compute a shock to gross output rather than to GDP.

A.7 Aggregate total shocks with growth of the health sector

Here we make a different assumption about how to construct the total shock for occupations and industries. For industries, we assume that if they experience a positive demand shock, the industries are able to increase their supply to meet the new demand. Instead of Eq. (8) we use

Since occupations are employed by different industries, the total shock to an occupation can be influenced by positive demand shocks from the healthcare sector and negative demand shocks from non-essential industries. In Eq. (9) we consider that occupations only experience the negative shocks. An alternative is to consider both the negative shock caused by non-essential industries and the positive shock caused by the health industries. This gives

In section IV, specifically Figure 8 , we use this convention for the y-axis, the Labour Shock. Using Eq. (14) allows us to observe how health-related occupations experience a positive shock.

In Table 6 we show the aggregate total shocks when using Eqs. (13) and (14). There is very little difference with the results in the main text. The health sector and its increase in demand are not large enough to make a big difference to aggregate results.

Main results allowing for growth in the health sector

Notes : The results are the same as those presented in Table 4 , but assuming that in industries, when demand is positive, the total shock is equal to the demand shock

In this section we give more details about how we constructed all our variables. We stress that our goal was to produce useful results quickly and transparently, and make them available so that anyone can update and use them. We intend to improve these estimates ourselves in the future, as more information becomes available on the ability to work from home, which industries are essential, and how consumers react to the crisis by shifting their spending patterns.

(i) Italian list of essential industries

The Italian list of essential industries 25 is based on the Statistical Classification of Economic Activities in the European Community, commonly referred to as NACE. Essential industries are listed with NACE 2-digit, 4-digit, and 6-digit codes. We automatically map industries listed at the 2- or 4-digit NACE level to NAICS 6-digit industries using the crosswalk made available by the European Commission. 26 The 6-digit NACE level classification is country-specific and thus there is no official crosswalk to NAICS codes. We map the 6-digit industries by hand. In some cases, a 6-digit industry NAICS code maps into more than one NACE industry code. When this happens, we consider the NAICS industry to be essential if it maps into at least one essential NACE industry code. We then build the essential score for industries at the NAICS 4-digit level; the essential score of a 4-digit NAICS industry is the fraction of NAICS 6-digit subcategories that are essential.

In a second step, we looked at the resulting list of 4-digit NAICS industries and their essential score and discovered a few implausible cases, resulting from the complex mapping between the various classification systems at different levels. For instance, because Transport is essential, ‘Scenic and sightseeing transportation, other’ was considered essential. In contrast, ‘Death care services’ was classified as non-essential. Three of us, as well as two independent colleagues with knowledge of the current situation in Italy, evaluated the list and we proceeded to editing the 4-digit NAICS essential scores as follows. From non-essential to essential: grocery stores; health and personal care stores; gasoline stations; death care services. From essential (sometimes only partly) to non-essential: scenic and sightseeing transportation; independent artists, writers, and performers; software publishers; motion picture and video industries; sound recording industries; and other amusement and recreation industries. Finally, owner-occupied dwellings and federal, state, and local government were not classified, and we classified them as essential.

(ii) Data for occupations

O*NET has work activities data for 775 occupations, out of which 765 occupations have more than five work activities. We compute the Remote Labour Index for the 765 occupations with more than five work activities. From the May 2018 Occupational Employment Statistics (OES) estimates on the level of 4-digit NAICS (North American Industry Classification System), file nat4d_M2018_dl , which is available at https://www.bls.gov/oes/tables.htm under All Data , we find data for the number of employed workers of 807 occupations in 277 industries. These data cover 144m workers. 27 From the sample of 765 occupations with RLI, and from the sample of 807 occupations with employment data from the BLS, we are able to match 740 occupations, which cover 136.8m workers. Therefore, our final sample has 740 occupations and 136.8m workers. 28

With the occupation-industry employment data and the essential score of each industry, we estimate the share of essential jobs within each occupation. Additionally, we have wage information for most occupations (i.e. we have median and mean wage data for 732 and 737 occupations). We computed all correlations for median wage considering all occupations we had median wage data for. For the three occupations for which median wage data were missing, the colour coding of occupations in Figures 4 , 7 , and the x-axis in Figure 8 corresponds to the average (across all occupations) of the median wage. We used the mean wages and the employment of occupations to define the wage quartiles of our sample. We excluded the three occupations for which we did not have mean wage data from these calculations.

Left : Relationship between Remote Labour Index and median wage. The Pearson correlation is 0.46 (p-value = 5.6 × 10 –39 ). Right : Relationship between fraction of workers in essential industries and wage. The Pearson correlation is 0.36 (p-value = 1.5 × 10 –24 ).

Left : Distribution of the RLI for the 740 occupations. Right : Distribution of the share of essential jobs within each of the 740 occupations

Left : Supply shock distribution across occupations. Right : Demand shock distribution across occupations.

Left : Shock distribution for occupations. Right : Distribution of exposure to disease

Finally, we use the O*NET data on exposure to disease and infection of occupations for the colour coding in Figure 8 . We explain these data further in   Appendix C . In the following charts we show the distribution of the RLI, exposure to disease and infection, supply, demand, and overall shocks across occupations.

(iii) Data for industries

Matching all data to bls i-o industries.

A key motivation of this paper is to provide relevant economic data which can be used by other researchers and policy-makers to model the economic impact of the COVID-19 pandemic. We therefore bring the supply and demand shock data into a format that matches directly to US input–output data.

We use the BLS 2018 input–output account, which allows us to discern 179 private sectors. There are the additional industries Private Households , NAICS 814, and Postal Service , NAICS 491. The data also contain 19 different industries relating to governmental activities. Since these industries are not classified with NAICS codes, we aggregate all governmental industries into a single node Government , which can be interpreted as the NAICS 2-digit industry 92. This leaves us with 182 industry categories which are a mixture of 2- to 6-digit NAICS industries. Moreover, the data contains one special industry ‘Owner-occupied dwellings’ which is not classified by NAICS codes yet relevant for GDP accounting.

We are able to match occupational data to 170 out of the 182 industry categories, accounting for 97 per cent of total value added (excluding Owner-occupied dwellings). For this subset we compute industry-specific RLIs, essential scores, and supply shocks as spelled out in   Appendix A .1, as well as employment-weighted infection exposures.

Since we have demand shocks only at the 2-digit NAICS level, disaggregating them into the more fine-grained BLS input–output data is straightforward when assuming that the demand shocks hold equally for all sub-industries.

In the online data repository, we also report total wages and total employment per industry. We use the same OES estimates as for the occupational data, but match every industry category according to the corresponding NAICS 2- to 6-digit digit levels.

Figures 14 to 16 show distributions of supply and demand shock-related variables on the industry level. Table 7 summarizes a few key statistics for these industries, when further aggregated to 72 industry categories.

Key statistics for different 2- and 3-digit NAICS industries

Notes : Column ‘Outp.’ refers to total output of the industry in current billion US$ (2018). ‘Emp.’ is total employment in thousands. ‘Demand’ is the immediate severe demand shock in % obtained from the CBO. ‘Supply’ is the immediate supply shock in % derived from the Remote Labour Index and the list of essential industries. ‘RLI’ is the industry-specific Remote Labour Index in %. ‘Essent.’ is the share of sub-industries being classified as essential in %. ‘Expos.’ denotes the industry-aggregated infection exposure index from O*NET which ranges from 0 to 100 and is explained in   Appendix C . We make more disaggregated data with further details available in the corresponding data publication.

Left : Distribution of the Remote Labour Index, aggregated to 170 industries. Right : Fractions of essential sub-industries per industry category

Left : Supply shock distribution across industries. Right : Demand shock distribution across industries

Shock distribution across industries

C. Occupations most at risk of contracting SARS-CoV-2

O*NET makes available online work context data for occupations that describe the physical and social factors that influence the nature of work. The ‘Exposed to disease and infection’ work context, 29 which we refer to as ‘exposure to infection’ for short, describes the frequency with which a worker in a given occupation is exposed to disease or infection. It ranges from 0 to 100, where 0 means ‘never’ and 100 ‘every day’; an exposed to infection rating of 50 means an exposure of ‘once a month or more but not every week’ and 75 means ‘Once a week or more but not every day’. We have exposure to infection data for 737 of the 740 occupations in our sample. For those occupations for which we did not have the exposure to infection, we coloured them as if they had zero exposure to infection.

As we see in Figure 17 , there is a U-shaped relationship between wages and exposure to infection. There is a correlation of 0.08 (p-value = 0.02) between wages and exposure to infection, but this is misleading. 30 Though many high-wage occupations are highly exposed to infection (highly paid doctors), there are also many low-wage occupations with high probability of infection.

Relationship between wage and probability of infection

Notes : The Pearson correlation is 0.08 (p-value =   0.2 ). However, we consider that this correlation is mostly driven by high salaries in the health sector, but there are many low-wage occupations with a significant exposure to infection.

D. Discussion of labour supply shocks which we do not include

(i) labour supply shocks from mortality and morbidity, typical estimates.

McKibbin and Fernando (2020) consider attack rates (share of population who become sick) in the range 1–30 per cent and case-fatality rates (share of those infected who die) in the range 2–3 per cent. From attack rates and case fatality rates, they compute mortality rates. They also assume that sick people stay out of work for 14 days. A third effect they assume is that workers would be care-givers to family members.

For their severe scenario of an influenza pandemic, Congressional Budget Office (2006) assumed that 30 per cent of the workers in each sector (except for Farms, which is 10 per cent) would become ill and would lose 3 weeks of work, at best, or die (2.5 per cent case fatality rate).

Best guess for current effect of COVID-19

In the case of COVID-19, estimating a labour supply shock is made difficult by several uncertainties. First of all, at the time of writing there are very large uncertainties on the ascertainment rate (the share of infected people who are registered as confirmed cases), making it difficult to know the actual death rate.

We report the result from a recent and careful study by Verity et al. (2020) , who estimated an infection fatality ratio of 0.145 per cent (0.08–0.32) for people younger than 60, and 3.28 per cent (1.62–6.18) for people aged 60 or more. The age bracket 60–69, which in many countries will still be part of the labour force, was reported as 1.93 per cent (1.11–3.89).

Taking the infection fatality ratio for granted, the next question is the attack rate. In Verity et al. (2020) , the infection fatality ratios are roughly one-fourth of the case fatality ratios, suggesting that three-quarters of the cases are undetected. For the sake of the argument, consider Italy, a country that has been strongly affected and appears to have reached a peak (at least of a first wave). There are at the time of writing 132,547 cases in Italy. 31 In 2018 the population of Italy 32 was 60,431,283. If we assume that Italy is at the peak today and the curve is symmetric, the total number of cases will be double the current number, that is 265,094, which is 0.44 per cent of the population. If we assume that the true number of cases is four times higher, the attack rate is, roughly speaking, 1.76 per cent. These numbers are more than an order of magnitude smaller than the number who cannot work due to social distancing.

Thus, while it is clear that the virus is causing deep pain and suffering throughout Italy, the actual decrease in labour supply, which is massive, is unlikely to be mostly caused by people being sick, and is much more a result of social distancing measures.

Uncontrolled epidemic

Now, it may be informative to consider the case of an uncontrolled epidemic. If we assume that the uncontrolled epidemic has an attack rate of 80 per cent (a number quoted in Verity et al. (2020) ), an infection fatality ratio for people in the labour force of 1 per cent (an arbitrary number between 0.145 per cent for people younger than 60, and 1.93 per cent for the 60–69 age bracket) implies an 0.8 per cent permanent decrease of the labour force. If we assume that those who do not die are out of work for 3 weeks, on an annual basis of 48 worked weeks, we have (3/48)*(0.80–0.01)=4.94 per cent decrease of the labour supply.

Overall, this exercise suggests that left uncontrolled, the epidemic can have a serious effect on labour supply. However, in the current context, the effect on the economy is vastly more a result of social distancing than direct sickness and death.

(ii) Labour supply shocks from school closure

School closures are a major disruption to the functioning of the economy as parents can no longer count on the school system to care for their children during the day.

Chen et al. (2011) surveyed households following a school closure in Taiwan during the H1N1 outbreak, and found that 27 per cent reported workplace absenteeism. Lempel et al. (2009) attempted to estimate the cost of school closure in the US in case of an influenza pandemic. They note that 23 per cent of all civilian workers live in households with a child under 16 and no stay-at-home adults. Their baseline scenario assumes that around half of these workers will miss some work leading to a loss of 10 per cent of all labour hours in the civilian US economy, for as long as the school closure lasts.

Some of these effects would already be accounted for in our shocks. For instance, some workers are made redundant because of a supply or demand shock, so while they have to stay at home to care for their children, this is as much a result of labour and supply shocks as a result of school closure. For those working from home, we might expect a decline in productivity. Finally, for those in essential industries, it is likely that schools are not closed. For instance, in the UK, schools are opened for children of essential workers. Our list of essential industries from Italy includes Education.

Overall, school closures indeed have large effects, but in the current context these may already be accounted for by supply and demand shocks, or non-existent because schools are not fully closed. The loss of productivity from parents working from home remains an open question.

E. Additional estimates of demand or consumption shocks

In this appendix we provide additional data on the demand shock. Table 8 shows our crosswalk between the industry classification of the Congressional Budget Office (2006) and NAICS 2-digit industry codes, and, in addition to the ‘severe’ shocks used here, shows the CBO’s ‘mild’ shocks. We have created this concordance table ourselves, by reading the titles of the categories and making a judgement. Whenever NAICS was more detailed, we reported the CBO’s numbers in each more fine-grained NAICS.

Mapping of CBO shocks to NAICS 2-digits

We also provide three sources of consumption shocks (in principle, these estimates are meant to reflect actual decreases in consumption rather than shifts of the demand curve). Table 9 shows the consumption shocks used by Keogh-Brown et al. (2010) to model the impact of potential severe influenza outbreak in the UK. Table 10 shows the consumption shocks used by Muellbauer (2020) to model the impact of the COVID-19 on quarterly US consumption. OECD (2020) provided two other sources, both reported in Table 10 . The first one is based on assumptions of shocks at the industry level, while the other shows assumptions of shocks by expenditure categories (COICOP: Classification of individual consumption by purpose).

Demand shock from Keogh-Brown et al. (2010)

Notes : The first column gives the percentage decrease, while the second column indicates whether some of the shock will be recouped in future quarters.

Estimates of consumption shocks from various sources

The aim of this paper was a timely prediction of first-order shocks before relevant data became available. While realized consumption is in principle different from demand shocks, it is instructive to look at the various studies of sectoral consumption that have appeared since our first paper.

Baker et al. (2020) use transaction-level data from a non-profit Fintech company to measure changes in consumption behaviour in the US. They find an increase in consumer spending at the early stage of the pandemic due to stockpiling, and sharp declines in most consumption categories in the subsequent weeks with public transportation, air travel, and restaurants experiencing the largest impacts.

Based on a survey of roughly 14,000 respondents, Coibion et al. (2020) analyse spending in several consumption categories as well as plans to buy durable goods. They find negative changes in all categories with the largest drops in travel, clothing, debt payments, and housing, and a decline in total spending by around 30 log percentage points.

Using daily data on bank card transactions of the second largest Spanish bank, Carvalho et al. (2020) study changes in consumption behaviours for 2.2m merchants between 1 January 2019 to 30 March 2020. Their results indicate that total consumption was rising before the enactment of the nationwide lockdown, and drastically falling thereafter (almost by 50 per cent compared to previous year levels). They also note substitution effects from offline to online payments. In line with our analysis they find substantial adjustments in the market shares of different expenditure categories. While categories like food shops and supermarkets, tobacconists, and pharmacies have experienced the largest increase in the consumption basket, restaurants, night clubs, furniture stores, and clothes shops experienced the largest decline in relative importance.

Analysing transactions of one million credit card users in Japan, Watanabe (2020) shows that aggregate consumption declined by 14 per cent. Travel spending experienced the largest decrease of 57 per cent. Declines in spending on goods tend to be smaller than declines in the consumption of services, with supermarkets and e-commerce experiencing even positive consumption impacts.

Using Chinese offline transaction data, Chen et al. (2020) find a substantial decrease in both goods and services consumption of around one-third. In line with other studies, the largest drops are in dining and entertainment as well as in travel, falling by 64 per cent and 59 per cent, respectively. Consumption response varies in magnitude for different Chinese cities, depending on how strongly they have been affected by the pandemic.

Andersen et al. (2020) analyse transaction-level customer data from the largest bank in Denmark. They estimate consumption levels to be 27 per cent below counterfactual levels without the pandemic. For retail, restaurants, and travel they find consumption drops of 24.7, 64, and 84.5 per cent, respectively, and report an increase of 9 per cent in grocery consumption.

Finally, according to the Opportunity Insights Economic recovery tracker ( Chetty et al. , 2020 ) 33 , compared to January 2020, US consumption fell very sharply up to 33 per cent before starting a slow recovery. Out of the six categories tracked, Groceries is the only sector exhibiting an increased consumption, while Health care, in sharp contrast to our predicted demand shocks, shows an impressive fall, by up to 58 per cent.

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This paper was prepared in March and early April 2020 and released on 16 April. This version contains only minor changes rather than comprehensive updates.

The results reported here are a slightly updated version of our work released on 16 April; we use a revised and updated list of essential industries, and we no longer exclude owner-occupied dwellings (imputed rents) from GDP (we assume no shocks to this sector). Our overall results do not change substantially.

See   Appendix D1 . for rough quantitative estimates in support of this argument.

https://www.insee.fr/en/statistiques/4473305?sommaire=4473307

In the future we intend to redo this using O*NET’s ‘detailed’ work activity data, which involve over 2,000 individual activities associated with different occupations. We believe this would somewhat improve our analysis, but think that the intermediate activity list provides a good approximation. All updates will be made available in the online data repository (see footnote 6).

An activity was considered to be able to be performed at home if three or more respondents rated this as true. We also undertook a robustness analysis where an activity was considered to be able to be performed at home based on two or more true ratings. Results remained fairly similar. In post-survey discussion, we agreed that the most contentious point is that some work activities might be done from home or not, depending on the industry in which they are performed.

https://zenodo.org/record/3744959

Available at https://www.onetcenter.org/crosswalks.html

We omitted ten occupations that had fewer than five work activities associated with them. These occupations include Insurance Appraisers Auto Damage; Animal Scientists; Court Reporters; Title Examiners, Abstractors, and Searchers; Athletes and Sports Competitors; Shampooers; Models; Fabric Menders, Except Garment; Slaughterers and Meat Packers; and Dredge Operators.

There are a few cases that we believe are misclassified. For example, two occupations with a high RLI that we think cannot be performed remotely are real estate agents (RLI = 0.7) and retail salespersons (RLI = 0.63). However, these are exceptions—in most cases the rankings make sense.

We use the May 2018 Occupational Employment Statistics (OES) estimates on the level of 4-digit NAICS (North American Industry Classification System), file nat4d_M2018_dl , which is available at https://www.bls.gov/oes/tables.htm under All Data . Our merged dataset covers 136.8 out of 144 million employed people (95 per cent) initially reported in the OES.

Mapping NACE industries to NAICS industries is not straightforward. NACE industry codes at the 4-digit level are internationally defined. However, 6-digit level NACE codes are country specific. Moreover, the list of essential industries developed by Italy involves industries defined by varying levels of aggregation. Most essential industries are defined at the NACE 2-digit and 4-digit level, with a few 6-digit categories thrown in for good measure. As such, much of our industrial mapping methodology involved mapping from one classification to the other by hand. We provide a detailed description of this process in   Appendix B.1 .

In fact we allow for a continuum between the ability to work from home, and an industry can be partially essential.

Since relevant economic variables such as total output per industry are not extensively available on the NAICS 4-digit level, we need to further aggregate the data. We derive industry-specific total output and value added for the year 2018 from the BLS input–output accounts, allowing us to distinguish 170 industries for which we can also match the relevant occupation data. The data can be downloaded from https://www.bls.gov/emp/data/input-output-matrix.htm .

Since rents account for an important part of GDP, we make an additional robustness check by considering the Real Estate sector essential. In this scenario the supply and total shocks drop by 3 percentage points.

As before, Table 6 in   Appendix A.7 gives the results assuming positive total shocks for the health sector, but shows that it makes very little difference.

https://www.onetonline.org/find/descriptor/result/4.C.2.c.1.b

Our demand shocks do not have an increase in retail but, in the UK, supermarkets have been trying to hire several tens of thousands of workers ( Source : BBC, 21 March, https://www.bbc.co.uk/news/business-51976075 ). Baker et al. (2020) document stock-piling behaviour in the US.

Employment Rate, aged 15–64, all persons for the US (FRED LREM64TTUSM156N) fell from 71.51 in December 2007 to 68.23 in June 2009, the employment peak to trough during the dates of recession as defined by the NBER.

Our data repository is at https://zenodo.org/record/3744959 , where we will post any update.

https://esd.ny.gov/guidance-executive-order-2026

Note that we column-normalize M to map from industries to occupations and row-normalize when mapping from occupations to industries.

To be clear, this is a product market supply-side shock, but this translates into a reduction in labour demand in each occupation.

Our estimate of GDP is the sum of VA of industries in our sample.

Available at http://www.governo.it/sites/new.governo.it/files/dpcm_20200322.pdf , 22 March.

https://ec.europa.eu/eurostat/ramon/relations/index.cfm?TargetUrl=LST_REL&StrLanguageCode=EN&IntCurrentPage=11

The US economy had 156m workers mid-2018, see https://fred.stlouisfed.org/series/CE16OV

Note that the BLS employment data we use here do not include self-employed workers (which currently accounts for about 16m people).

https://www.onetonline.org/find/descriptor/result/4.C.2.c.1.b?s=2

For example, Adams-Prassl et al. (2020) , using survey evidence for ~4,000 US individuals, found that workers without paid sick leave are more likely to go to work in close proximity to others, which may have suggested a negative correlation between wages and exposures. Note, however, that our correlation is based on occupations, not individuals, and that wages are not necessarily an excellent predictor of having paid sick leave or not.

https://coronavirus.jhu.edu/map.html

https://data.worldbank.org/indicator/SP.POP.TOTL?locations=IT

https://tracktherecovery.org , accessed 27 June 2020

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The Law of Supply and Demand: Here It Is Finally

17 Pages Posted: 19 Aug 2014 Last revised: 19 Apr 2015

Egmont Kakarot-Handtke

University of Stuttgart - Institute of Economics and Law

Date Written: August 17, 2014

There is no such thing as a law of human or social behavior. The conceptual consequence of this paper is to discard the subjective-behavioral axioms and to take objective-structural axioms as formal foundations. The central piece of economic theory is the interaction of supply and demand which determines prices and quantities. Supply and demand in turn are assumed to be determined by subjective factors. In the structural axiomatic paradigm the Law of Supply and Demand follows solely from objective factors. The Law consists of measurable variables and is testable in principle. The results prove the superiority of the new paradigm.

Keywords: new framework of concepts, structure-centric, axiom set, harmonic structure

JEL Classification: B59, D40

Suggested Citation: Suggested Citation

Egmont Kakarot-Handtke (Contact Author)

University of stuttgart - institute of economics and law ( email ).

Keplerstrasse 17 Stuttgart Germany

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Improvement in battery technologies as panacea for renewable energy crisis

• Open access
• Published: 09 July 2024
• Volume 6 , article number  374 , ( 2024 )

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• Hafeez Ajibade 1 ,
• Chika Oliver Ujah 1 , 2 ,
• Kingsley C. Nnakwo 3 &
• Daramy V. V. Kallon 2  

This review article explores the critical role of efficient energy storage solutions in off-grid renewable energy systems and discussed the inherent variability and intermittency of sources like solar and wind. The review discussed the significance of battery storage technologies within the energy landscape, emphasizing the importance of financial considerations. The review highlighted the necessity of integrating energy storage to balance supply and demand while maintaining grid system stability. The review thoroughly explored the characteristics and applications of lead-acid and lithium batteries. It drew distinctions and emphasized their safety and application advantages. The comparative review covered key factors, including cycle life, power density, energy density, efficiency, and cost considerations. Additionally, the article explored future trends, emerging technologies, and research directions. The findings of the review revealed that each battery technology offers unique advantages, presenting a challenge in selecting a preferred option for diverse applications. Furthermore, the review discussed the Total Cost of Ownership (TCO) of batteries, considering factors beyond the initial investment, with a specific emphasis on economic viability. This comprehensive approach provided a valuable tool for making informed decisions regarding the adoption of battery storage solutions in the pursuit of a sustainable energy future.

Avoid common mistakes on your manuscript.

1 Introduction

1.1  background.

The current global interest in renewable energy sources is primarily driven by the necessity to reduce reliance on fossil fuels and mitigate environmental degradation caused by conventional energy generation technologies. In a report published in June 2023 [ 1 ] by the International Energy Agency (IEA), it was projected that solar photovoltaic and wind energies would account for unprecedented major annual growth in additional renewable energy capacity. In 2023, the largest growth globally was projected to occur from 107 GW to over 440 GW, with solar PV accounting for a 66% share. This development is expected to grow further in the year 2024. This trend has become a global movement with far-reaching benefits. These benefits range from preserving the natural ecosystem to mitigating health risks and ensuring energy independence.The abundance of these renewable energy sources ensures sustainability for generations and fosters a global responsibility to reduce the effects of climate change. This has the potential to foster innovation and grow economies. The incorporation of renewable sources, such as geothermal, wind, and solar, into current energy systems becomes paramount in the global quest for more sustainable energy for years to come. This necessarily requires the need to efficiently utilize energy and reduce losses as much as possible, as practiced in conventional energy generation systems [ 2 ]. This paper reviewed the strategic function of battery storage systems in the integration of renewable energy into current energy systems.

From time to time, the industry evolves with newer battery technology, and each time, they are commercially promoted as better technologies than the existing ones. Notwithstanding the higher investment costs, field reports on the deployment of newer technologies do reveal salient challenges that may not have been envisaged. Challenges such as the additional cost of providing a suitable environment for deployment, professional services, and monitoring are typical, especially in developing nations. This usually causes users to fall back on the good old, trusted technology. The launch of the NASA Vanguard 1 satellite on March 17, 1958, with the deployment of solar cells for power generation, and the harvested energy stored in batteries [ 3 ], marking a significant leap in the deployment of lead-acid batteries for energy storage. Over time, new technologies like NiCad, alkaline, and the recent lithium batteries were developed, but lead-acid batteries continue to be relevant in many applications despite the advantages offered by newer technologies. In fact, the lead-acid industry too has evolved over the century with improvements in technology. This study could elucidate the reasons for the sustained relevance of lead-acid technology in today’s energy storage applications. The study could also reveal some lessons new technology could adopt from the lead-acidindustry to enhance competitiveness and a larger market share.

1.2 Novelty and key contribution

Renewable integration is a major challenge in developing countries like Nigeria, where inadequate power generation is compounded with instability in the grid, making the integration of renewables very challenging. Most governments resort to off-grid applications which will necessarily require energy storage solutions. Depreciating currencies and rising inflationary trends increase sharply the cost of imported items. Design engineers are therefore faced with the options of designing battery storage technology either based on performance or cost. Performance favors newer technologies, while cost favors older technologies. This study aims to strike a balance between performance and cost in the design decisions on battery energy storage systems for practitioners in developing nations which rely on importation of electrochemical storage technologies. Striking a balance between commercial promotion and the reality of deploying batteries for renewable energy storage will allow professionals and users to make informed decisions on the choice of battery. This study discusses economic viability, in addition to technology, as a means of evaluation for design engineers, when taking decisions on the suitability of battery technology in the context of the application. Such decisions are able to balance both technical and financial goals within prevailing circumstances. This study has not been reported elsewhere in the literature. Hence, the motivation is routed on the imperative of this discussion and its timeliness.

This study limits its scope to developing economies that rely heavily on off-grid renewable energy systems to improve access to electricity. Batteries considered are the lead-acid and the lithium technologies. These technologies are matured and have been successful ones in the industry for many years. The study does not cover battery technologies for mobile devices, automotive and electric vehicle applications.

1.4 Article transition

The review highlighted the crucial role of energy storage solutions, especially in off-grid renewable energy systems, emphasizing the importance of battery technologies for grid stability and balancing supply and demand. It thoroughly compared lead-acid and lithium batteries, discussing their characteristics, safety, advantages, and key factors: cycle life, power density, energy density, efficiency, and cost considerations. The paper acknowledged the enduring relevance of lead-acid batteries for reliability and cost-effectiveness, while also recognizing the unique advantages of lithium batteries. Both technologies are poised for advancements, focusing on sustainability, safety, and performance, with project-specific decisions prioritizing the option with the lowest total cost of ownership. The work was arranged in the following manner: Introduction of the topic containing background study, motivation, novelty and scope and methodology adopted in the work formed the first section of this review. Section  2 discusses renewable energy and its challenges in integrating it into the mainstream grid. Section  3 discusses overview of lead acid batteries and lithium-ion batteries technologies. In Sect. 4, battery storage principles and practice were discussed. Section 5 discusses critical findings of the review work, while Sect.  4.8.4 discusses the conclusion.

1.5 Methodology

The review type adopted in this study was mixed studies review which combined both literature review and mixed studies [ 4 ]. Relevant articles were downloaded from Scopus database and google scholar database. A total of 200 peer-reviewed articles were downloaded. Thereafter, 70 were discarded and 130 were used for the study. Among the articles used were book chapters, conference proceedings, as well as research articles.

2 Renewable energy integration challenges

2.1 variability and intermittency of renewable sources.

While renewable energy sources offer a clean and sustainable solution to the global energy crisis, their inherent variability and intermittency pose challenges to their harvesting and utilization. They are prone to variability in energy production due to the time of day, weather conditions [ 5 ], and the effects of climate change. For example, a solar power plant produces energy that is closely correlated with the presence of sunshine during the day. Variations in the sun’s position, cloud cover, and seasonal weather cause fluctuations in solar energy output. The same variations in output are also true for wind turbines concerning the availability of wind resources.

To ensure steady and reliable energy supply to load centers, grid operators must manage the challenges posed by fluctuation and intermittency. Mismatches between energy supply and demand may result in abrupt declines or spikes in the output, which might potentially cause blackouts or grid instability. When renewables are integrated into the grid, variability and intermittency create instability, making energy storage a desired solution [ 6 ]. In the case of off-grid systems, energy storage systems are deployed to store excess energy when production is at its peak for use when generation is low or not available [ 7 ]. They mitigate the erratic nature of renewable energy generation, thereby enabling the efficient and effective utilization of energy produced at peak periods or seasons.

2.2 Renewable energy systems and energy storage

Transmission conductors, in conventional energy generation, form the backbone of efficient energy delivery to load centers [ 8 ]. Similarly, batteries play a pivotal role in storing electricity for renewables, allowing for efficient storage and retrieval. Advanced battery technologies, like lithium-ion, are increasingly utilized for surplus energy storage and released when renewable sources are not actively generating electricity [ 9 ]. Batteries provide rapid response times and high discharge rates, addressing the variability of renewable energy production.Energy storage systems should also be economically viable to promote adoption, especially for commercial purposes. The increasing deployment of large-scale battery storage projects worldwide underscores the importance of energy storage in renewable energy systems. Additionally, they facilitate the integration of a larger proportion of renewable energy into existing power generation infrastructure, reducing reliance on fossil fuels and decreasing greenhouse gas emissions [ 10 ].

It is indisputable that energy storage is crucial for reducing the unpredictability and intermittent nature of renewable sources. Energy storage technologies not only solve the problem of intermittency but also support the deployment of renewable energy at scale, in line with global efforts aimed at carbon footprint reduction and combating climate change [ 11 ]. As theworld moves closer to a low-carbon energy future, energy independence and improved grid dependability, the global transition to a resilient and sustainable energy landscape can be facilitated, in part, by the integration of storage solutions into renewable energy systems.

3 Overview of battery technologies

Chemistry and electricity combine through electrochemical processes to produce precise products [ 12 ]. Secondary cells or batteries store and release an electrical energy through reversible electrochemical processes. Chemical energy is first converted into electrical energy, and alternately, electrical energy is converted to chemical energy. This process involves the movement of charged particles (ions) between two electrodes through an electrolyte [ 13 ]. Oxidation occurs at the anode, the electrode where, during discharge, electrons are released to the external circuit. In the reverse reaction, reduction occurs at the cathode, the electrode where, during discharge, electrons are accepted from the external circuit. The electrolyte completes the electrical circuit, allowing ions to flow between the anode and cathode.

3.1 Lead-acid battery chemistry

Lead-acid batteries are one of the oldest and most widely used rechargeable battery technologies [ 14 ]. They are renowned for their high reliability and cost-effectiveness. The chemistry of lead-acid batteries involves reversible electrochemical reactions that occur within cells. During discharge, chemical energy converts to electrical energy, and during charging, the reverse occurs. This chemistry involves reversible reactions between lead oxide (PbO2), lead (Pb), and sulfuric acid (H2SO4) in an aqueous electrolyte [ 15 , 16 , 17 , 18 , 19 ], illustrated in Fig.  1 .

Lead-acid battery chemistry [ 19 ]

The cathode, positive plate, is made of lead oxide while the anode, negative plate, is made of sponge lead material. Chemical reactions take place at the two electrodes during discharge and charge cycles as shown in Eqs. 1–6.:

Discharge reactionat the (lead oxide) Cathode.

Lead oxide(PbO 2 ), goes through reduction reaction:

Discharge reaction at the (lead) Anode.

Lead (Pb), goes through oxidation reaction:

Overall cell discharge reaction:

The overall discharge reaction combines these two half-reactions:

Chemical energy stored in the reactants is converted to electrical energy.

Charging reaction (reverse of discharge):

The charging process is characterized by an external voltage applied to the battery, causing reverse reactions to occur:

Electrical energy is used to drive the chemical reactions where electrical energy is converted to chemical energy stored in the battery.

Electrolyte (sulfuric acid):

The sulfuric acid (H 2 SO 4 ) in the electrolyte serves multiple purposes. It provides the medium for ions to flow between the two plates during discharging and charging. Additionally, it plays a key function in lead sulfate (PbSO 4 ) formation on both plates.

Lead sulfate formation:

Lead sulfate (PbSO4) forms on both plates during discharging, subsequently breaking down during charging. The formation and dissolution of lead sulfate are crucial for the reversible nature of the lead-acid battery.

Lead sulfate is dissolved during the process of charging:

And the formation of lead sulfate during discharge:

Lead acid batteries have a long-standing track record amongst the oldest and well established technologies for storing energy. Theyhave been a staple in renewable energy storage applications for decades, providing a high round-trip efficient and cost-effective solution for capturing and storing electricity generated from intermittent renewable sources. This comparative review explores recent research papers on three lead-acid battery technologies: Flooded Lead-Acid (FLA), Valve Regulated Lead Acid (VRLA), and Lead-Carbon. The analysis will delve into the key characteristics, advancements, and challenges associated with each type.

3.1.1 Flooded lead-acid batteries

FLA batteries, the traditional workhorses of the energy storage industry, have been extensively researched for their performance in renewable energy applications. The liquid electrolyte is enclosed in a vented casing that allows for escape of gases during charging, and addition of distilled water after charging. Figure  2 shows a typical flooded lead acid battery.

Flooded lead acid battery [ 19 ]

In the study by Wagner [ 20 ], flooded lead-acid batteries were found to have an extensive track record, having been utilized in various applications for decades. The manufacturing process was explored, which involves immersing lead plates in an electrolyte solution, typically sulfuric acid. This simple yet robust construction contributes to their widespread use in applications like renewable energy storage, uninterruptible power supplies (UPS), and backup power systems. In another paper by Wang et al. [ 21 ], advancements in flooded lead-acid battery technology were explored, focusing on improvements in plate design and separator materials. The research demonstrated better cycle life and charge retention, making FLA batteries more suitable for demanding renewable energy storage applications. The study also addressed environmental concerns, proposing recycling methods to reduce the environmental impact of lead-acid batteries.

Lu et al. [ 22 ] explored the importance of understanding and managing the electrolyte stratification phenomenon in flooded lead-acid batteries. The paper highlighted how stratification, caused by different charge and discharge rates, can impact the battery’s efficiency and cycle life. Strategies for mitigating stratification, such as optimized charging profiles, were discussed. In a study by Crown Battery [ 23 ], the life expectancy of FLA batteries was affected by factors such as depth of discharge, temperature, and maintenance practices, while FLA batteries generally had a moderate cycle life. The paper further advised how longevity can be extended through proper care, regular maintenance, and controlled operating conditions. Cycling characteristics of FLA batteries make them suitable for applications requiring occasional deep discharges, such as in off-grid solar installations.

Equalization, a controlled overcharge process, is essential for FLA batteries to address cell imbalances and sulfation. Gallardo-Lozano et al. [ 24 ] posited that equalization benefits include improving charge distribution among cells, minimizing stratification, and mitigating the risk of premature failure. This process contributes to enhanced performance and increased overall battery life. Sulfation, the buildup of hard lead sulfate crystals on battery plates, is a common issue in FLA batteries. Manwell et al. [ 25 ] investigated this phenomenon and observed that it occurs when batteries are consistently undercharged or subjected to prolonged periods of inactivity. Sulfation negatively impacts battery performance by reducing capacity and increasing internal resistance. It suggested regular maintenance, proper charging practices, and periodic equalization could help mitigate sulfation issues.

Several failure modes can affect flooded lead-acid batteries. Leung et al. [ 26 ] and Yang et al. [ 27 ] in separate technical publications listed plate corrosion, electrolyte stratification, and shedding of active material as common issues leading to reduced performance and premature failure. The research established that overcharging, excessive cycling, and exposure to high temperatures can exacerbate these failure modes. Recovery methods for FLA batteries aimed to address performance degradation and extend their service life. Banguero et al. [ 28 ] and Roy et al. [ 29 ] in their study revealed that equalization (controlled overcharging) is a primary method to desulfate (dissolving hard lead sulfate crystals) in order to restore capacity. Additionally, desulfation devices and additives were employed to break down the sulfate crystals and rejuvenate the battery. Regular maintenance, monitoring electrolyte levels, and temperature control are integral aspects of successful recovery methods.

3.1.2 Valve regulated lead acid (VRLA) batteries

VRLA batteries are designed as sealed maintenance free, with immobilized electrolyte (Fig.  3 ). They come in two variants, Absorbent Glass Mat (AGM) and Gel batteries. They have gained popularity due to their maintenance-free design and suitability for various renewable energy storage applications.

VRLA batteries [ 30 ]

Rand et al. [ 31 ] agree that VRLA batteries have gained widespread acceptance since their introduction, finding applications in diverse fields such as renewable energy systems, uninterruptible power supplies (UPS), and telecommunications. In another study, Weighall [ 32 ] also noted that the manufacturing process involves sealing the battery, limiting the release of gases, and incorporating a pressure-relief valve to regulate internal pressure. A comparative analysis by Chen et al. [ 33 ] evaluated the performance of AGM and Gel batteries in terms of cycle life, charge retention, and efficiency. The publication identified AGM batteries as more suitable for high-rate applications, such as photovoltaic systems with intermittent high-power demands. Furthermore, a study by Gupta et al. [ 34 ] delved into the advancements in VRLA battery technology to address limitations such as acid stratification and thermal management. The paper explored the use of advanced materials for separators and the impact on improving charge-discharge performance. The findings showcased the prospects of VRLA batteries to contribute significantly to the reliability and efficiency of renewable energy systems.

Chang et al. [ 35 ] believed that the “maintenance-free” feature claimed for VRLA batteries can be a misrepresentation of the actual design. This claim has led many users to neglect VRLA batteries leading to initial internal problems and eventual battery failure. VRLA batteries still require maintenance activities like the provision of a temperature-controlled environment at not more than 25 °C, keeping charging voltage within specifications, and providing a battery monitoring system. The life expectancy of VRLA batteries is affected by factors like operating conditions, charging practices, and ambient temperature, as noted by Hatanaka et al. [ 36 ]. These batteries exhibit favorable cycling characteristics, making them suitable for applications requiring frequent charge-discharge cycles. Svoboda et al. [ 37 ] advised that proper charging practices and temperature control contribute to optimizing their life expectancy. In research by May et al. [ 38 ], VRLA batteries demonstrated sensitivity to ambient temperature, with performance being affected by extremes in heat or cold. Fairweather et al. [ 39 ] also contributed to the discussion noting high temperatures can accelerate chemical reactions, leading to increased water loss and accelerated aging, while low temperatures can result in reduced capacity and slower chemical reactions. Proper temperature management is crucial to ensuring optimal VRLA battery performance.

Sulfation, the formation of lead sulfate on battery plates, remains a concern for VRLA batteries according to Crown Battery [ 40 ]. Although the sealed design minimizes electrolyte evaporation, sulfation can still occur, impacting capacity and performance. Yahmadi et al. [ 41 ] posited that regular maintenance and appropriate charging practices are essential to mitigate sulfation-related issues. In a study carried out by Wagner [ 42 ], common failure modes in VRLA batteries include plate corrosion, electrolyte stratification, and thermal runaway. While the sealed design minimizes the risk of acid leakage, internal failures can still occur, leading to diminished performance and premature failure. Jung et al. [ 43 ] explored how overcharging, excessive cycling, and manufacturing defects contribute to failure modes in VRLA batteries. Nakamura et al. [ 44 ] in a study noted that recovery methods for VRLA batteries primarily focus on preventive measures, as the sealed design limits the accessibility for traditional maintenance practices while Crown Battery’s recommendations [ 40 ] such as ensuring proper charging practices, regular capacity testing, and monitoring internal resistance are integral to extending the life of VRLA batteries. Desulfation devices and controlled charging can also be employed to address sulfation-related issues.

3.1.3 Lead-carbon batteries

Lead-carbon batteries, a hybrid of VRLA chemistry and carbon additives at the electrodes, have become an impressivelead acid technology for storage of renewable energy. Figure  4 shows a typical battery design.

Lead carbon battery [ 45 ]

Zhang et al. [ 46 ], in a more elaborate review, explored the unique features of lead-carbon batteries, including improved cycle life and charge-discharge efficiency. The paper discussed the role of carbon additives in mitigating sulfation, a common issue in lead-acid batteries, and enhancing overall performance. Moreover, research by Yanamandra et al. [ 47 ] investigated the impact of carbon nanostructure additions to the negative plate. The study revealed that incorporating nanostructured carbon materials improved cycle life and enhances retention capacity. These findings contributed to the ongoing optimization of lead-carbon battery technology for renewable energy applications. Huang et al. [ 48 ], in a study, noted that lead-carbon batteries have gained attention for their potential in renewable energy integration and grid-scale applications. Calábek et al. [ 49 ] reviews showed that the manufacturing process involves incorporating additives of carbon to the negative electrode, providing benefits such as improved charge acceptance and reduced sulfation.

The life expectancy of lead-carbon batteries is influenced by factors like charge-discharge cycles, depth of discharge, and operational conditions, as observed by Yogeswari et al. [ 50 ]. These batteries exhibit favorable cycling characteristics, showing potential for applications requiring frequent charge-discharge cycles. The addition of carbon enhances their performance, contributing to longer life expectancy. Lu et al. [ 51 ], in a study, noted that lead-carbon batteries demonstrated resilience to ambient temperature variations, making them suitable for a range of climates. The combination of lead-acid and carbon technologies mitigates some of the temperature sensitivity observed in traditional lead-acid batteries. This characteristic enhances their performance in diverse environmental conditions. Bao et al. [ 52 ] were able to identify why sulfation remains a concern for lead-carbon batteries, albeit to a lesser extent compared to traditional lead-acid batteries. Moseley et al. [ 53 ], in a review, discussed extensively how the presence of carbon additives helps to mitigate sulfation issues by providing a conductive framework that facilitates charge acceptance, reducing the risk of lead sulfate accumulation on the battery plates.

Ball et al. [ 54 ] and Li et al. [ 55 ], in their studies, noted that common failure modes in lead-carbon batteries include electrolyte stratification, electrode corrosion, and thermal runaway, while carbon additives contribute to improved performance. Challenges related to the interface between the carbon and lead components may still lead to failure modes if not properly addressed. According to Pavlov &Nikolov [ 56 ], recovery methods for lead-carbon batteries focus on optimizing charge-discharge cycles, preventing sulfation, and ensuring proper maintenance practices. Controlled charging, regular capacity testing, and monitoring internal resistance are vital aspects of recovery methods. In research by Sadhasivam et al. [ 57 ], carbon-enhanced electrodes contribute to effective recovery by facilitating improved charge acceptance.

3.1.4 Comparative analysis

When comparing the aforementioned lead-acid battery technologies, several key factors come to light. Firstly, flooded lead-acid batteries have demonstrated their reliability and effectiveness over decades. Their simple design, cost-effectiveness, and well-established maintenance practices contribute to their continued relevance in various applications. However, challenges such as electrolyte stratification and environmental concerns continue to drive research for improvements. Understanding their cycling characteristics, equalization benefits, sulfation issues, and recovery methods is essential for optimizing performance and maximizing the life expectancy of FLA batteries.

VRLAbattery technology is established as a reliable and convenient energy storage solution in various applications. Their sealed design, maintenance-free operation, and favorable cycling characteristics make them well-suited for scenarios where traditional flooded lead-acid batteries may be less practical. Advances in separator materials and charging profiles contribute to their suitability for diverse renewable energy storage scenarios. However, considerations for ambient temperature effects, sulfation issues, and potential failure modes necessitate careful management and monitoring to ensure optimal performance and longevity. Nevertheless, the trade-off between cycle life and high-rate performance remains a consideration in the selection process.

Lead-carbon batteries, a relatively newer entrant, represent a significant advancement in lead-acid battery technology, offering improved cycling characteristics and a reduced risk of sulfation. This represents improved lead acid characteristics with respect to enhanced efficiency and extended cycle life. The incorporation of carbon additives, especially nanostructured materials, demonstrates a pathway to further optimizing their performance. The hybrid nature of lead-carbon batteries positioned them as a potential bridge between traditional lead-acid and advanced lithium-ion technologies. While challenges related to failure modes persist, current efforts in research and development seek to optimize the performance and longevity of lead-carbon batteries.

3.2 Lithium batteries

3.2.1 lithium batterychemistry.

Lithium-ion (Li-ion) batteries have become ubiquitous in various applications requiring energy storage like mobile devices, electric vehicles and renewable energy systems. Basically, the chemistry of lithium-ion batteries relies on the movement of lithium ions, during charge and discharge cycles, between the positive electrode and the negative electrode [ 58 , 59 , 60 , 61 , 62 , 63 , 64 ], as shown in Fig.  5 .

Lithium ion battery chemistry [ 64 ]

This process is facilitated by the use of different materials for the electrodes and an electrolyte solution containing lithium salts. Detailed below is the lithium battery chemistry based on key components.

Cathode (positive electrode):

The lithium-ion cathode is usually made of lithium metal oxide material, typically oxides of Lithium Cobalt (LiCoO 2 ), Lithium Manganese (LiMn 2 O 4 ), Lithium Nickel Manganese Cobalt (LiNiMnCoO 2 or NMC), and Lithium Iron Phosphate (LiFePO 4 ). The discharge process starts with the movement of lithium ions from the cathode towards the anode via the electrolyte and separator material.

Anode (negative electrode):

Lithium-ion anode is typically composed of graphite material, although silicon and other materials are also being researched for their potential to enhance energy density. The discharge process is characterized by movement of lithium ions from the anode to the cathode through the electrolyte. The anode goes through a process known as intercalation, where lithium ions are implanted into the anode material crystal lattice structure.

Electrolyte:

Electrolytes in lithium-ion are made from salts of lithiumdissolved in a solvent. Salts such as Lithium hexafluorophosphate (LiPF 6 ), Lithium tetrafluoroborate (LiBF 4 ), andlithium perchlorate (LiClO 4 ) are common electrolyte materials. The solvent is usually a combination of diethyl carbonate (DEC), dimethyl carbonate (DMC), ethylene carbonate (EC). The electrolyte serves as a means for the movement of lithium ions between the electrodes. The choice of electrolyte affects the temperature stability, performance and battery safety.

In Lithium-ion battery, the separator is a membrane designed to be thin and porous. It physically separates the anode from the cathode. It is positioned toallow the flow of lithium ions whilst preventing direct contact and short circuits between the two electrodes.Due to the excellent performance of polypropylene and polyethylene, they are commonly used as separator materials.

Lithium-ion battery working principle:

During discharge, when the battery is providing electrical power, lithium ions move through the electrolyte from the anode to the cathode. Simultaneously, electrons flow through the circuit connected externally, creating an electric current. Lithium ions at the cathode are intercalated into the crystal lattice of the cathode material.

Anode (discharge):

Cathode (discharge):

During charging, in a reversed process, lithium ions migrate from the cathode back to the anode, just as electrons flow back into the anode.

Anode (charge):

Cathode (charge):

Lithium-ion has emerged as a dominant technology in renewable energy storage, offering improved efficiency, long cycle life, and high energy density. Within this realm, two prominent types are Lithium Nickel Manganese Cobalt Oxide (NMC), and Lithium Iron Phosphate (LFP). This comparative review aims to explore recent research papers on LFP and NMC battery technologies, focusing on key characteristics, advancements, and associated challenges.

3.2.2 Lithium iron phosphate (LFP) batteries

LFP are typically assembled in packs containing, battery rack, batteries and battery management system (BMS) as shown in Fig.  6 [ 64 ].

50kWh LFP battery bank [ 64 ]

In a study, Li & Ma [ 65 ], advanced that LFP batteries have established a robust track record, especially in renewable energy storage and electric vehicles (EVs). Liu et al. [ 66 ], was able to break down the manufacturing process of LFP which includes combining lithium iron phosphate cathodes with graphite anodes, and an electrolyte, forming a stable and high-performance lithium-ion battery. Dunn et al. [ 67 ], in a study, recorded LFP batteries to be exhibiting an impressive life expectancy, often surpassing 2000 cycles. Their inherent stability and resistance to thermal runaway contribute to prolonged cycle life. In an experimental study by Krieger et al. [ 68 ]. , , LFP batteries demonstrated excellent cycling characteristics, making them suitable for applications requiring frequent charge-discharge cycles, such as grid storage and electric vehicles. Ma et al. [ 69 ] explored the temperature characteristics of LFP batteries and results showed a robust performance across a wide range of ambient temperatures. The thermal stability of LFP chemistry contributes to their suitability in various climates. Their ability to maintain high performance even in extreme temperatures makes them attractive for diverse applications globally.

Etacheri et al. [ 70 ] noted that sulfation is generally minimal in LFP batteries due to their stable cathode chemistry. The absence of traditional lead-acid battery sulfation issues contributes to the long-term reliability of LFP batteries. Their inherent resistance to sulfation enhances their suitability for applications where intermittent use is common. Kaliaperumal et al. [ 71 ], in a review, deduced that common failure modes in LFP batteries are primarily related to overcharging, which can lead to thermal runaway and reduced capacity. However, LFP batteries are inherently safer than some other lithium-ion chemistries, and rigorous battery management systems (BMS) are implemented to prevent these failure modes. A review by Hendricks et al. [ 72 ] concluded that the use of Failure Modes, Mechanisms, and Effects Analysis (FMMEA) to improve test and design in LFP batteries can help realize higher system safety and reliability. Various studies show that recovery methods for LFP batteries focus on preventing overcharging, optimizing BMS settings, and ensuring balanced cell performance. While LFP batteries are known for their stability, preventive measures are crucial to extending their already impressive cycle life. Okay et al. [ 73 ], in a study, noted that advanced BMS technology contributes to effective recovery by maintaining optimal operating conditions.

LFP batteries have garnered attention for their exceptional safety and thermal stability. A study by Aravindan et al. [ 74 ] emphasized how the intrinsic safety of LFP cathodes is attributed to the robustness of the Fe–O bond. The paper discussed how the unique crystal structure of LFP contributes to its lower susceptibility to thermal runaway reactions, making it a safer option for renewable energy storage applications. Research by Li et al. [ 75 ] delved into the optimization of LFP battery performance by exploring the effect of particle size on cycling stability. Further revelations showed that smaller particle sizes contributed to improved lithium-ion diffusion kinetics and enhanced cycle life. The findings suggested strategies for tailoring LFP electrode materials to achieve superior electrochemical performance. Zhang et al. [ 76 ] explored the specific application of LFP batteries in renewable energy systems, particularly in conjunction with solar photovoltaic installations. The paper discussed the compatibility of LFP batteries with intermittent energy sources, emphasizing their ability to provide a stable and reliable storage solution. The research further highlighted the significance of LFP batteries in supporting the integration of renewable energy into the power grid.

3.2.3 Lithium nickel manganese cobalt oxide (NMC) batteries

NMC batteries also come in packs of battery rack, batteries and battery management system (BMS) as shown in Fig.  7 [ 77 ].

NMC battery bank [ 77 ]

According to Liu et al. [ 78 ], NMC batteries have demonstrated a strong track record, particularly in electric vehicles and portable electronics. In a study by Lu et al. [ 79 ], the manufacturing process involved combining lithium, nickel, manganese, and cobalt oxides to create cathodes with varying compositions. This flexibility allows tailoring NMC batteries to specific application requirements. Research by Sahana&Gopalan [ 80 ] suggested that NMC batteries exhibit competitive life expectancy, with advancements in electrode design contributing to prolonged cycle life. The ability to modify the nickel, manganese, and cobalt ratios allows optimizing the trade-offs between energy density and cycle life. NMC batteries show promising cycling characteristics, suitable for both consumer electronics and electric vehicle applications. NMC batteries demonstrates moderate sensitivity to ambient temperatures, with performance influenced by both high and low-temperature extremes. A detailed description of this behavior can be found in a recent study by Li et al. [ 81 ]. Advances in thermal management systems contribute to mitigating temperature-related challenges. NMC batteries remain a preferred choice for applications with controlled operating conditions. Cheng et al. [ 82 ] were able to successfully establish that sulfation, while generally less pronounced in NMC batteries compared to lead-acid chemistries, can still occur and impact performance. The design of NMC batteries with appropriate voltage ranges and advanced battery management systems helps minimize sulfation risks. Regular maintenance practices and controlled charging contribute to mitigating sulfation-related issues.

In a study by Zheng et al. [ 83 ], common failure modes in NMC batteries include thermal runaway, electrode degradation, and capacity fade overcharging, while high operating temperatures can contribute to these failure modes. Ongoing research focuses on improving electrode design, electrolyte formulations, and safety features to address potential failure modes and enhance overall battery reliability [ 84 , 85 , 86 , 87 ]. Recovery methods for NMC batteries emphasize preventive measures, such as optimizing charging protocols and implementing advanced battery management systems, as detailed by Lei et al. [ 88 ] in a study. Innovations in materials science and cell design contribute to mitigating common failure modes. A recent study by Lipu et al. [ 89 ] concluded that controlled charging and discharging, along with proper thermal management, play pivotal roles in the recovery and long-term performance of NMC batteries. NMC batteries, known for their high energy density, have been a focal point of research aiming to enhance their performance further. A comprehensive review by Manthiram et al. [ 90 ] outlined the evolution of NMC cathodes, exploring various nickel, manganese, and cobalt compositions. The paper discusses the challenge of balancing between structural stability and energy density, providing insights into the challenges associated with optimizing NMC chemistry.

Xu et al. [ 91 ] investigated the fast charging capabilities of NMC batteries, an essential aspect for renewable energy systems requiring rapid energy storage replenishment. The study explores the influence of charging rates on the electrochemical characterization of NMC electrodes, offering valuable insights into the design considerations for achieving high-power performance without compromising cycle life. Given the potential for thermal issues in high-energy-density systems, studies such as that carried out by Wang et al. [ 92 ] focused on thermal management strategies for NMC batteries. The paper explores the effectiveness of different cooling methods in mitigating temperature-related degradation mechanisms. Understanding and controlling thermal effects are essential for ensuring safety and durability of NMC batteries in renewable energy applications.

3.2.4 Comparative analysis

Safety and stability:

LFP batteries, with their iron phosphate cathodes, are recognized for their inherent safety and thermal stability. The robust Fe–O bond structure contributes to a lower risk of thermal runaway reactions. On the other hand, NMC batteries, while offering higher energy density, may pose challenges related to thermal management due to the presence of nickel, which can contribute to increased reactivity.

Cycle life and performance:

LFP batteries benefit from smaller particle sizes, enhancing lithium-ion diffusion kinetics and extending cycle life. In NMC batteries, the challenge lies in balancing energy density with structural stability, and research is focused on optimizing cathode compositions to achieve superior electrochemical performance.

Applications in renewable energy systems:

LFP batteries are well-suited for renewable energy systems, particularly in solar installations, due to their stability and reliability. The ability of LFP batteries to integrate seamlessly with intermittent energy sources aligns with the requirements of renewable energy applications. NMC batteries, with their high energy density, are also applicable in renewable energy systems, providing ample storage capacity for fluctuating energy generation.

Cost considerations:

NMC batteries may have higher initial cost relative to LFP batteries, primarily due to the use of materials like cobalt. However, ongoing advancements are reducing the cost gap. NMC batteries are commonly found in applications where a balance between cost and energy density is crucial, electric vehicles and certain grid storage projects are typical examples.

3.3 Comparative analysis in performance metrics betweenlead acid batteries and Lithium batteries

3.3.1 energy density.

Lead acid batteries normally have energy density that is lower relative totheir lithium counterpart.The limited energy densitycan be a constraint in applications where space and weight considerations are critical [ 93 ].Lithium batteries exhibit significantly higher energy density, offering a superior capacity to store energy per unit weight and volume. This characteristic makes lithium batteries more suitable for applications demanding high energy density [ 94 ], such as renewable storage and electric vehicles.

3.3.2 Power density

Lead acid batteries typically have lower power density than lithium batteries [ 95 ]. This implies that lead acid batteries may have limitations in delivering high power outputs in applications requiring rapid charge and discharge cycles.Lithium batteries excel in power density, enabling them to provide high power outputs efficiently. This feature is advantageous in applications like power tools and electric vehicles, where quick bursts of energy are essential [ 96 ].

3.3.3 Cycle life

Cycle life in lead acid is generally lower compared to lithium batteries [ 97 ]. This naturally limits the number of charge and discharge cycles that lead acid batteries can undergo, making them less suitable for applications requiring long-term, repetitive use.Lithium batteries demonstrate a longer cycle life, making them more durable and reliable over an extended period. This characteristic is crucial in applications where batteries are subjected to frequent charge and discharge cycles, such as renewable energy storage.

3.3.4 Efficiency

Lead acid batteries may have lower efficiency compared to lithium batteries [ 98 ], especially in terms of charge and discharge efficiency. This could result in energy losses during the charging and discharging processes.Lithium batteries are known for their higher charge and discharge efficiency, minimizing energy losses during power transfers. This efficiency is advantageous in various applications, contributing to overall system performance.

3.3.5 Cost considerations

Lead acid batteries are generally more cost-effective upfront compared to lithium batteries. The lower initial cost makes lead acid batteries a preferred choice in applications where cost is a primary concern [ 99 ].Lithium batteries have a higher investment cost relative to lead acid batteries.Nonetheless, advancements in technology and increased production volumes are gradually reducing the cost gap, making lithium batteries more economically viable over the long term [ 100 ].

3.3.6 Environmental impact

Lead acid batteries can have a higher environmental impact due to the use of lead, a toxic heavy metal. Proper recycling and disposal practices are essential to mitigate environmental risks associated with lead acid batteries [ 101 ].Lithium batteries, while generally considered to be friendlierto the environment than lead acid batteries, pose challenges in terms of resource extraction and disposal. Improved recycling methods and sustainable sourcing practices are crucial to minimize the environmental footprint of lithium batteries [ 102 ].

3.3.7 Application/Suitability

Lead acid batteries are suitable for storage solutions where cost is a primary consideration, and lower energy and power densities are acceptable. Common applications include uninterruptible power supplies (UPS), backup power systems, and stationary energy storage for renewable sources [ 103 ].Lithium batteries find widespread use in applications demanding high energy and power densities, such as grid-scale renewable energy storage, electric vehicles and portable electronics. Their superior performance features make them suitable for demanding and flexible applications [ 104 ].

3.4 Total cost of ownership

Energy storage systems are playing pivotal roles in renewable energy in ensuring the reliability and stability of power supply from intermittent sources. Assessing the total cost of ownership (TCO) of batteries in these applications is crucial for evaluating their economic feasibility over the entire lifecycle. TCO encompasses various factors beyond initial costs, including maintenance, replacement, and operational expenses.

Initial investment:

The upfront cost of batteries constitutes a significant portion of the TCO. Different battery chemistries have varying initial costs based on factors like energy density and technology maturity.

Operational and maintenance costs:

Operational expenses include the costs associated with maintaining and operating the energy storage system. This involves monitoring, cooling systems, and periodic maintenance to ensure optimal performance. VRLA, lead carbon batteries [ 105 ] and Lithium-ion batteries generally have lower maintenance costs compared to FLA batteries due to their sealed design and extended cycle life [ 106 ].

Round-trip energy efficiency:

Energy efficiency, expressed as the proportion of output energy to input energy, influences the TCO. Batteries with higher round-trip efficiency minimise energy losses during charge and discharge cycles, leading to lower operational costs over time. Lithium-ion batteries often exhibit higher round-trip efficiency compared to lead acid batteries, contributing to their economic advantage [ 107 ].

Cycle life and degradation:

The number of charge and discharge cycles a battery is able to undergo before significant degradation is a critical factor. Batteries with longer cycle life often have a lower TCO as they require less frequent replacements. Lithium-ion batteries, particularly certain chemistries like Lithium Iron Phosphate (LFP), commonly offer much better cycle life relative to lead acid batteries [ 108 ].

Charging and discharging characteristics:

The efficiency and performance of energy storage system are influenced by the charging and discharging characteristics. Rapid charge and discharge capabilities, especially in lithium-ion batteries, can enhance the overall system efficiency, contributing to a lower TCO [ 109 ].

End-of-life and recycling:

Recycling of batteries and proper disposal after End of Life (EOL) is a significant aspect of TCO. Lithium-ion batteries may pose challenges in terms of recycling due to complex chemistries, while lead acid batteries have established recycling processes with high recovery rates [ 110 ].

Economic viability over time:

Evaluating batteries for their economic viability involves considering a number of factors like cost per kilowatt-hour ($/kWh) stored, the cost per cycle, and the total capacity over the system’s life. Advances in battery technology, economies of scale, and research and development efforts contribute to the continuous improvement of TCO for various battery chemistries [ 111 ].

4 Battery storage: the offing

4.1 future trends and innovations.

Future trends in lead acid industry include improvements in advanced technologies, such as enhanced FLA and VRLA batteries [ 112 ].Innovations will focus on optimizing existing lead-acid chemistries to improve performance, cycle life, and efficiency. Future trends for lithium batteries involve continuous innovations to enhance energy density, safety, and cost-effectiveness. Lithium-air andlithium-sulfur batteries are emerging as potential breakthrough technologies, offering higher energy densities and addressing environmental concerns [ 113 ].

4.2 Emerging battery technologies

Emerging technologies for lead acid batteries include advanced lead-carbon, which aim to improve upon charge acceptance and cycle life while reducing environmental impact [ 114 ]. Lithium-silicon and lithium-solid-state batteries are emerging as breakthrough technologies for lithium batteries, promising higher energy density, faster charging, and improved safety features [ 115 ].

4.3 Research and development directions

Research in lead acid technologycenters on advanced materials, like carbon additives and modified lead alloys, to enhance performance and reduce environmental impact [ 116 ]. Research in lithium batteries is directed towards developing new cathode and anode materials, exploring solid-state electrolytes, and optimizing cell designs for improved safety and performance [ 117 ].

4.4 Market trends

Lead acid batteries continue to dominate markets for specific applications like automotive starting, backup power and stationary energy storage due to their cost-effectiveness [ 118 ]. Lithium batteries are experiencing significant market growth, driven by the increasing demand for grid-scale energy storage, electric vehicles and portable electronics.

4.5 Industry outlook

The lead acid battery industry is evolving to meet modern energy storage needs, with a focus on improving performance, recycling processes, and exploring new applications. The lithium battery industry is dynamic, with a strong emphasis on scaling production, reducing costs, and addressing concerns related to resource availability and environmental impact [ 119 ].

5 Review outcomes

5.1 chemistry and characteristics.

Two battery chemistries commonly employed in renewable energy applications were identified. Lead acid batteries are based on acid electrochemistry with Deep Cycle FLA, VRLA and Lead-Carbon types being favorites for renewable energy storage applications. Overall, deep cycle FLA batteries, VRLA batteries (AGM and Gel), and lead carbon batteries each have unique characteristics and advantages, making them suitable for various applications drawing strength from features like cycling, operational efficiency and maintenance requirements. The choice of battery type is influenced by specific requirements of the solution and the available budget.On the other hand, lithium batteries, based on alkaline electrochemistry haveLFP and NMC as the widely applied technologies for renewable energy storage [ 120 ]. Both LFP and NMC batteries offer unique advantages in tackling specific challenges in the context of renewable energy storage. LFP batteries stand out for their safety and stability, making them well-suited for applications where these attributes are critical. NMC batteries, with their high energy density, offer greater storage capacity but necessitate careful thermal management to address potential challenges.

5.2 The future oflead acid and lithium batteries

Going by the increase in demand for storage solutions for renewable energies, both lead acid and lithium technologies, just like photovoltaic and hydrogen storage technologies [ 121 , 122 ], are undergoing significant advancements in research and development to address key challenges and improve overall performance. A cursory look into the lead acid industry reveals innovative formulations and manufacturing techniques are at the forefront of developments. Researchers are exploring the integration of carbon additives, particularly in lead-carbon batteries, to enhance charge acceptance, reduce sulfation, and improve efficiency. Advanced plate designs and construction methods are also being investigated to boost energy density and overall efficiency. The incorporation of smart battery management systems (BMS) is another noteworthy trend, offering improved monitoring, optimized charging algorithms, and preventive maintenance to increaseservice life. Moreover, a strong emphasis on recycling technologies aims to address environmental concerns and promote sustainable practices in the industry.

Lithium batteries are witnessing a dynamic landscape of innovations. The evolution towards high-nickel cathodes, exemplified by NMC 811, represents a major trend aiming to improve energy density while simultaneously reducing costs. Solid-state lithium batteries were developed as groundbreaking innovation, promising higher energy density, extended cycle life and improved safety, overexisting lithium-ion batteries. Silicon anodes, with their higher capacity compared to traditional graphite anodes, contribute to increased energy density in lithium batteries. Furthermore, the integration of advanced electrolytes and the rise of artificial intelligence (AI)-enabled battery management systems highlight the commitment to optimizingperformance, safety, and efficiency in lithium battery technologies. Sustainability is also a key focus, with ongoing efforts directed towards efficient methods for recycling lithium-ion batteries and minimizing environmental impact.

While lead acid batteries are exploring advancements in carbon additives, smart BMS, and recycling technologies, lithium batteries are witnessing a paradigm shift with innovations in high-nickel cathodes, solid-state technology, silicon anodes, and AI-enabled management systems. Both technologies are evolving to meet the diverse and expanding needs of applications, including renewable energy storage, mobile devices, and electric vehicles.

5.3 The resilience and relevance of lead acid batteries in modern times

Lead acid batteries continue to demonstrate resilience and relevance in modern times despite being a first generation battery technology. While lithium-ion batteries have captured significant attention, lead acid batteries maintain a strong foothold in various applications due to their unique set of advantages, making them competitive in today’s diverse energy storage landscape. The technology has stood the test of time and continues to compete in modern times. Its proven track record, cost-effectiveness, recyclability, robustness, and suitability for specific applications contribute to its ongoing relevance. As technology evolves, lead acid batteries find their niche, complementing other energy storage solutions and demonstrating that, in the diverse landscape of modern energy storage, there is room for both traditional and cutting-edge technologies.

5.4 Application and suitability

The choice between lead acid batteries and lithium batteries is influenced by the actualrequirements of the application. Lead acid batteries may be more appropriate in cost-sensitive applications with lower energy and power density needs, while lithium batteries offer superior performance in applications requiring higher efficiency, longer cycle life, and increased energy and power densities. These technologies represent two distinct approaches to energy storage, with theirdistinctivebenefits and drawbacks. While lead acid batteries continue to be relevant in specific applications due to their cost-effectiveness per kWh, lithium batteries are driving innovation and dominating markets with their superior energy density and versatility.Beyond technical aspects, the financial feasibility of an energy storage solution plays a pivotal role. Factors such as government incentives, tax credits, and ongoing advancements in manufacturing that reduce costs can significantly impact the financial feasibility of adopting specific technologies. Understanding the economic landscape and available financial support is crucial in determining the TCO.

5.5 Total cost of ownership

Recognizing the important role total cost of ownership (TCO) playsin renewable energy storage applications is vital for making informed decisions regarding battery technology selection and system design [ 123 ]. While both battery technologies have reciprocal advantages over each other, the choice of the most economically viable solution depends on specific project requirements, including energy storage capacity, operational conditions, initial investment, operational and maintenance costs, round-trip efficiency, cycle life, end-of-life, recycling, and economic viability over time. In the quest to achieve efficiency and sustainability inlithium ion battery energy storage [ 124 ], a comprehensive TCO analysis that encompasses both technical and financial considerations is essential. Technologies like lithium-ion batteries, with their evolving chemistries and favorable performance metrics, often present attractive TCO propositions. However, the suitability of an energy storage solution depends on particular applications, operational specifications, and the prevailing economic context. A careful assessment that balances technical and financial feasibility is integral to making informed decisions in the dynamic landscape of energy storage solutions.

6 Conclusion

This review underscored the enduring relevance of lead-acid battery technologies in achieving a harmonious balance between reliability, cost-effectiveness, and environmental sustainability, particularly in medium to large-scale storage applications within the evolving renewable energy landscape. Future research directions are poised to enhance these technologies, focusing on optimization, innovative materials, and targeted solutions to meet the growing storage demands of renewable energy applications.On the other hand, the more recent lithium battery technologies emerge as unique solutions, addressing challenges related to greater storage capacity, high energy density, stability, and safety. The increased emphasis on battery monitoring and meticulous thermal management reflects the evolving landscape. The future trajectory of battery technology appears promising, with advancements expected in both lead-acid and lithium-based systems, maintaining a focus on sustainability, safety, and performance. Ongoing investigations will further explore applications like grid-scale energy storage, propelling the continuous evolution of lithium battery technologies.Both lead-acid and lithium-based systems are well-positioned in their respective niche areas, signaling their sustained relevance. The future trajectory of battery technology appears promising with advancements expected in both lead-acid and lithium-based systems, with an unwavering focus on sustainability, safety, and performance. Project-specific battery choices will hinge on a thorough assessment of technical feasibility, followed by a comprehensive financial analysis considering all cost elements. The ultimate recommendation for battery technology adoption will prioritize the option with the lowest total cost of ownership.

Data availability

No datasets were generated or analysed during the current study.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hafeez Ajibade and Chika Oliver Ujah. The first draft of the manuscript was written by Hafeez Ajibade and supported by Daramy V.V. Kallon. The work was supervised by Chika Oliver Ujah and all authors commented on previous versions of the manuscript. The manuscript was edited by Kingsley Chidi Nnakwo. All authors read and approved the final manuscript.

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Ajibade, H., Ujah, C.O., Nnakwo, K.C. et al. Improvement in battery technologies as panacea for renewable energy crisis. Discov Appl Sci 6 , 374 (2024). https://doi.org/10.1007/s42452-024-06021-x

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A taxonomy of demand-driven questions for use by evidence producers, intermediaries and decision-makers: results from a cross-sectional survey

• Cristián Mansilla   ORCID: orcid.org/0000-0003-2377-0094 1 , 2 ,
• Arthur Sweetman 3 ,
• Gordon Guyatt 4 &
• John N. Lavis 1 , 4  

Health Research Policy and Systems volume  22 , Article number:  78 ( 2024 ) Cite this article

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Globally, a growing number of calls to formalize and strengthen evidence-support systems have been released, all of which emphasize the importance of evidence-informed decision making. To achieve this, it is critical that evidence producers and decision-makers interact, and that decision-makers’ evidence needs can be efficiently translated into questions to which evidence producers can respond. This paper aims to create a taxonomy of demand-driven questions for use by evidence producers, intermediaries (i.e., people working in between researchers and decision-makers) and decision-makers.

We conducted a global cross-sectional survey of units providing some type of evidence support at the explicit request of decision-makers. Unit representatives were invited to answer an online questionnaire where they were asked to provide a list of the questions that they have addressed through their evidence-support mechanism. Descriptive analyses were used to analyze the survey responses, while the questions collected from each unit were iteratively analyzed to create a mutually exclusive and collectively exhaustive list of types of questions that can be answered with some form of evidence.

Twenty-nine individuals completed the questionnaire, and more than 250 submitted questions were analysed to create a taxonomy of 41 different types of demand-driven questions. These 41 questions were organized by the goal to be achieved, and the goals were grouped in the four decision-making stages (i) clarifying a societal problem, its causes and potential impacts; (ii) finding and selecting options to address a problem; (iii) implementing or scaling-up an option; and (iv) monitoring implementation and evaluating impacts.

The mutually exclusive and collectively exhaustive list of demand-driven questions will help decision-makers (to ask and prioritize questions), evidence producers (to organize and present their work), and evidence-intermediaries (to connect evidence needs with evidence supply).

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Evidence has become a crucial component of decision-making processes and, by supporting decision-makers to address a broad variety of issues, from identifying problems to analysing potential solutions and evaluating the implementation of actions, it can play a significant role in several stages of the policy cycle [ 1 , 2 , 3 ].

In recent years, there has been a growing number of calls to coordinate and strengthen the global evidence architecture [ 4 , 5 , 6 ]. These calls stem from the recognition that evidence-informed decision making is essential for implementing better programs and policies, and that high-quality evidence is necessary for decision-making.

These calls have also stressed that there is a critical need to match and integrate the different forms of evidence to support the steps and varied needs in the decision-making process, and to further strengthen global evidence architecture. In this paper, we adopt the broad definition of evidence used by the Global Commission on Evidence to Address Societal Challenges [ 5 ], which includes all forms of decision-relevant evidence (data analytics, modelling, evaluation, qualitative insights, behavioural/implementation research, evidence syntheses, guidelines, and technology assessments).

Despite these global calls and the momentum created by the COVID-19 pandemic, there remains a continuing risk of mismatch between decision-makers’ needs and the evidence that is made available to support decision-makers [ 7 ]. There are several factors that can help to explain why decision-makers’ needs are not always fully addressed by research evidence [ 8 ]. One factor is that decision-makers have multiple evidence needs and the types of questions that are traditionally used by researchers are limited in scope [e.g., PICO (population, intervention, comparison, outcome) [ 9 ], SPIDER (sample, phenomenon of interest, design, evaluation, research type) [ 10 ], and PEO (population, exposure, outcome)].

It is critically important that decision-makers understand what types of question that evidence might usefully address, and that evidence producers and intermediaries (i.e., people working in between researchers and decision-makers) understand how to translate decision-makers’ needs into questions that can be used to address these needs [ 11 ]. Such understanding can help to build trust, promote more and better interactions, and increase the usefulness and use of existing evidence.

This paper aims to create a taxonomy of questions that evidence can help to answer. Specifically, it aims to:

Create a list of types of questions that decision-makers around the world have commonly asked of those they turn to for decision-relevant evidence.

Create a mutually exclusive and collectively exhaustive list of such questions.

This study is a cross-sectional survey of evidence-support units providing evidence support to decision-makers. These units provide evidence-related advice to decision makers on a timely and regular manner. The study aims to collect different types of questions that decision-makers regularly ask, to identify the wide range of questions where evidence could provide decision-relevant insights, and to develop a mutually exclusive and collectively exhaustive taxonomy of types of questions. This study was approved by the Hamilton Integrated Research Ethics Board (HiREB), Project ID: 8279.

Participants

Between March and May 2022, representatives of evidence-support units were invited to answer a questionnaire, which was administered online via a link provided by email to each participant. We understand an evidence-support unit as a group that provides timely, demand-driven summaries of what’s known and not known—based on the best available research evidence—about a question facing decision-making. To be eligible, units needed to:

Answer questions in response to a request coming from decision-makers, including (but not necessarily limited to) government policymakers (i.e., units addressing real-life evidence needs from decision-makers).

Address issues that are not exclusively in the clinical domain (for health-focused units).

Have produced at least five evidence-informed answers in the last 5 years (i.e., the unit is or has recently been active).

Participants that did not produce evidence-support at an explicit request of decision-makers, or that were only focused on clinical answers were excluded from this study.

Representatives of existing evidence-informed policymaking networks, the most recent of these being EVIPNet, were identified and contacted to verify if they were eligible to participate. These representatives were contacted and asked if they were filling the criteria described above to be eligible to participate in this study. Alternatively, they were also be asked if they were aware of other potentially eligible units.

Data collection

The online questionnaire requested the various types of questions that decision-makers regularly ask the unit and, when possible, for a more complete list of the questions they had previously addressed, a URL link to their products. The questionnaire also collected basic information regarding the scope of the work that each unit performs in supporting decision-making processes. The questionnaire was first piloted with two different centres to assess whether it was easy to complete or that the instructions would need further details.

The questionnaire was sent to participants, and one person per unit was eligible to answer. The questionnaire was originally written in English, but participants were also allowed to answer in French or Spanish if they felt more comfortable answering in those languages. The questionnaire is available in Additional file 1 .

Data analysis

The data collected in the survey were summarised using descriptive analyses and reported with absolute numbers and frequencies. For the questions that were provided by participants, many of them were very similar (e.g., effectiveness of a specific intervention). Hence, for each participant, the 10 most recent questions that each unit reported to have answered were collected aiming at capturing a broad variety of types of question.

Later, these questions were categorized in an iterative thematic analysis to create a mutually exclusive and collectively exhaustive list. If necessary, compound questions answered by these units were split into multiple fundamental questions, and questions were excluded if: (1) they were questins into which evidence cannot provide decision-relevant insights; (2) they were aiming to collect information about what other recommendations have said (e.g., what do scientific societies recommend about a given health condition?); (3) they were explicitly described as having not been asked by a decision-maker; and (4) they were addressed by building on other frameworks (e.g., agenda setting) that do not involve foreground evidence.

The initial draft taxonomy that was created from the responses and structured using the policy cycle framework [ 12 ]. In this process, types of questions and goals were created in an inductive way, while the stages were taken from an existing framework (i.e., policy cycle). Additionally, this original draft was complemented by using existing frameworks included in the Evidence Commission report [ 5 ], the GRADE Evidence to Decision (EtD) framework [ 13 ], and the Consolidated Framework for Implementation Research (CFIR) [ 14 ]. Finally, taking advantage of national, regional, and global meetings, a number of people were engaged in deliberations about how to improve the clarity and comprehensiveness of the taxonomy.

Twenty-seven units were initially identified as potentially eligible, and seven additional units were suggested by participants. Two participants either declined or were found to be ineligible to participate, leaving 32 final potential participants. Twenty-nine answers were received (response rate 90.6%), but only 20 provided a list of questions that could be extracted. In total, 1076 questions were provided. By sampling the 10 most recent questions that were addressed by participants, we analysed a total of 237 different questions.

Table 1 provides details about survey participants. The majority of the units surveyed were based in a university, national ministry, or non-governmental organization. While they accept requests from many types of actors, including government policymakers, managers and program implementers, they most commonly answer requests coming from mid-level policymakers and program implementers. Finally, they serve different domains within the health sector, namely clinical management, public health decisions, health-system (not including technology assessment) decisions and technology assessments.

Figure  1 shows the goals of each decision-making stage. In total, 41 different types of questions were identified and characterized as part of this taxonomy. To facilitate the understanding of the taxonomy, Tables 2 , 3 , 4 and 5 describe the types of questions included in each goal. A lay formulation of each goal is also provided in every table, and below. In each decision-making stage, to identify some concepts that are commonly used in certain disciplines to name specific types of questions, notes provide explanations of technical discipline-specific language. Additional file 2 presents a more detailed description of each type of question.

Taxonomy of demand-driven types of questions structured by decision-making stage

Stage 1. Clarifying a societal problem, its causes, and potential impacts

This stage aims to clarify a problem, identify potential causes, and outline potential impacts or spillover effects that this problem might create. It is organized into six different goals that may need to be achieved (A to F). In total, this stage includes 15 different types of questions that may need to be answered (Table  2 ).

Although ‘problems’ create a decision-making scenario that frames an issue in a negative way, an issue can also be framed in a positive way as objectives (or once a problem has been identified, it can also be framed positively as objective). Then, the goals included in this section can also be framed in a positive or more neutral way by replacing problems by objectives, such as: A. Choosing and prioritizing measurements to determine whether an objective has been reached; B. Describing an objective and its implications; C. Understanding an objective; D. Assessing variability of an objective and its implications; E. understanding the preliminary steps and critical opportunities to reach out an objective; and F. Understanding the impacts of achieving an objective. We will continue by describing this stage as a ‘problem’ assuming that, as mentioned here, the question can be easily formulated using neutral or positive rhetoric.

Problems may be issues that are in the present or the past, but they can also be issues that are not necessarily a problem now, but that could eventually become one (future problems, including existential risk). These future problems were not created as specific types of questions, acknowledging that the same types of questions that are included in this stage can be equally formulated for future problems.

Problems can also arise from issues created in other decision-making stages (e.g., no feasible option is available, an implementation strategy does not address a barrier, or the option has not had the impact that it should have had, or its impact failed to be sustained). In these cases, users of this taxonomy might consider the issue as a new problem and identify a question that could match this issue in this decision-making stage.

Questions related to people’s values and experiences (e.g., values regarding outcomes, understanding people’s perceptions, etc.) might also vary according to some social characteristics, such as socioeconomic status, ethnicity, etc., and these issues are somehow included in these types of questions.

Stage 2. Finding and selecting options to address a problem

This stage aims to find and select options that could address (or help to reduce) the impact of a problem. It is structured as four distinct goals that may need to be achieved (A to D). In total, this stage includes 13 different types of questions that may need to be answered (Table  3 ).

Similar to problems, options can be present or past interventions, or they can also be interventions that are not available right now but could become an option in the future. Specific questions for these issues were not created, acknowledging that the same types of questions that are included in this stage can be formulated for present for future options.

The types of question included here are in the context of options not yet implemented and it is their possible impact that is assessed. The actual impact of the implementation of an option in decision-making will be addressed in stage 4 (Monitoring implementation and evaluating impacts).

Identifying the equity, ethical and human rights implications of an option could be understood as whether the impact of the option had different implications depending on specific population characteristics (e.g., socioeconomic status, ethnicity, etc.).

Stage 3. Implementing or scaling-up an option

This stage aims to address issues related to the implementation of a given option. It is structured around two different goals that may need to be achieved (A and B). In total, this stage includes 6 different types of questions that may need to be answered (Table  4 ).

Implementing an option is a critical stage in the decision-making process. However, there are some interventions in which the implementation stage might not necessarily be critical (e.g., prescribing a clinical treatment course for a given hospitalized patient).

The conditions that an option requires to be implemented can be classified using behavioural (e.g., what individuals need to do for the option to be implemented) and/or contextual (that are often split in relevant to the inner and outer settings) variables. The contextual variables, and the setting (i.e., inner and/or outer setting), include the potential equity implications that the implementation of a given option might have.

Stage 4. Monitoring implementation and evaluating the impacts of an option or implementation strategy

This stage aims to monitor the implementation of a given option and to evaluate its causal impacts in a particular setting. It is structured as two different goals (A and B). This stage includes 7 different types of questions that may need to be answered (Table  5 ). Monitoring implementation and evaluating impacts can be done at the short, medium and/or long-term; identifying measurement strategies for problems and options are also a key part of this stage.

Principal findings and findings in relation to the existing literature

This paper develops a taxonomy of mutually exclusive and collectively exhaustive types of demand-driven questions in which evidence may provide decision-relevant insight. We identified forty different types of questions, which were classified across 14 different goals in four different decision-making stages. Some existing frameworks have been developed to formulate research questions, such as PICO [ 9 ] and SPIDER [ 10 ], or to understand what type of categories or typologies of research questions can be addressed by evidence syntheses [ 15 , 16 ], or facilitating models for the taxonomy of research studies[ 17 ]. However, these frameworks were not built with a demand-driven approach (complemented by existing frameworks as the one presented in this paper) to facilitate decision-making.

Although the field of knowledge translation has substantially evolved in recent decades, knowledge translation efforts and tools have concentrated on how new research findings can be better disseminated to decision-makers [ 18 ]. However, no available tools facilitate the interaction between decision-makers and evidence producers or intermediaries (i.e., people working in between researchers and decision-makers) at the question-formulation stage to achieve a more responsive evidence-support system.

A recently renewed focus on the co-production of knowledge—understood as a collaboration between evidence producers, decision makers, and any other stakeholder to design, implement and interpret research for a given need [ 19 ]—has of course yielded outputs that can support the future flow of new research. This taxonomy provides a more actionable output, which could be used to help in co-produce evidence support. Hence, when a decision-making need emerges, collaborative work among decision-makers, evidence intermediaries and evidence producers facilitated by the taxonomy created in this paper might make easier to clarify the specific question for which an evidence-informed answer is required.

Strengths and limitations

This study has several strengths. First, this is the first paper that creates a mutually exclusive and collectively exhaustive list of types of question for which evidence could provide decision-relevant support. Secondly, the taxonomy was created using a demand-driven perspective by asking evidence-support groups to itemize the questions they have received from decision-makers. Hence, it is built from existing questions that have been addressed by at least one of a variety of operating evidence-support units. Finally, it uses generic language that facilitates the communication across different sectors/disciplines and different forms of evidence.

This study has also some limitations. First, it was infeasible to reach all the units that provide some type of support across all sectors and disciplines, and participants working in non-surveyed sectors might provide extensions to this taxonomy, which can affect the representativeness of the study population. Also, while this paper presents a mutually exclusive and collectively exhaustive list of types of question, it has not yet been applied to a specific setting or context to validate and facilitate the understanding of this taxonomy. Finally, despite the units that participated in this study provided demand-driven support, the questions received by them were the ones that they answered, which might not necessarily be the ones that they were requested to answer.

Implications for policy and practice

This taxonomy can have different implications depending on three main audiences. First, decision-makers (including government policymakers, professionals and citizens) could easily scan the different types of questions to clarify the type of questions for which evidence could provide decision support. Second, impact-oriented evidence producers of any form of evidence could better orient their work to organize and prioritize types of questions, enhancing coordination and avoiding duplication among them. Finally, this tool could strongly support evidence-intermediaries in connecting the demand needs with the supply side.

When using this taxonomy of types of question, users should bear in mind the following considerations. First, although we have presented the types of question in a logical order, they are by no means intended as a list each of which those making policy decisions should consider for each one of their issues. Indeed, decision-makers can use one, some, or all of the questions to address a given issue. By providing guidance on what questions from this taxonomy would most usefully be addressed to answer a specific decision or specific fields, evidence intermediaries could facilitate this selection.

Secondly, some types of question included might not be relevant for certain groups (e.g., comparing the importance of a problem against others in social sciences, or prioritizing spill over effects across different sectors). Thirdly, our aim in developing the taxonomy was to organize questions and not the results that research answering these questions could have. Hence, since they are essentially an assessment of the answer of a specific type of question, we considered questions such as “What are the evidence gaps or the methodological limitations of the existing evidence for a given topic?” out of the scope. Finally, there are several types of question that are addressed by building on other complex frameworks (e.g., agenda setting of a policy issue [ 20 ]; chances of a policy to be developed looking at institutions, interests and ideas [ 21 ] or the political economy; or the external validity of a given body of evidence). These questions are important, and several types of questions from the taxonomy could contribute to conducting an assessment in these complex frameworks.

Implications for future research

This taxonomy of research questions is only a first of many efforts that could facilitate the connection between demand-side needs and evidence production and support. Further research should explore how different study designs could properly answer each type of question identified in this taxonomy. A concrete application of this taxonomy in a case study would help to validate and test the tool. Matching types of decisions (e.g., funding a new technology, what intervention to use for addressing a specific problem, whether acting now is the right time, conducting or not a pilot for a new technology) with the types of questions included in this taxonomy would, by specifying what types of question in this taxonomy should be answered depending on the specific type of decision, facilitate a stronger and more integrated evidence-support system.

Future research efforts could also go back to the survey participants and interviewing: (1) a sample of them to ask whether they have encountered additional questions that were not represented in the taxonomy, because they have been addressed by complementary groups in other sectors, or in groups that provide a more integrated evidence-support to decision-makers in a given country; and (2) other actors (e.g., government policymakers, science advisors, subject-matter advisors, etc.) who could provide additional types of question that were not necessarily addressed by evidence advice.

Finally, future uses of the taxonomy in combination with artificial intelligence could consider these types of questions in their algorithms and quickly identify claims that are, or are not, supported by evidence.

Conclusions

This paper provides a unique taxonomy of 41 demand-driven types of questions where evidence could provide decision-relevant insights, structured around four decision-making stages (clarifying a societal problem, its causes and potential impacts; finding and selecting options to address a problem; implementing or scaling-up an option; and monitoring implementation and evaluating impacts). Decision-makers, evidence intermediaries, and impact-oriented evidence producers could importantly benefit from this taxonomy to facilitate the exchange of evidence needs from decision-makers, through evidence intermediaries and to better connect evidence-production efforts among evidence producers.

Availability of data and materials

The anonymized datasets used during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We acknowledge and thank all the individuals that participated giving their valuable feedback to make this taxonomy a list that would be useful to facilitate the better connection of research with decision making. They include Ludovic Reveiz, Michelle Haby, Kerry Albright, Tanja Kuchenmuller, Erik von Uexkull, Jeremy Grimshaw, Cristian Herrera, and the participants of the HSR 2022 Symposium, particularly Nasreen Jessani, Yodi Mahendradhata, Arash Rashidian, and Simon Lewin. We also thank Jennifer Verma, for her contributions in better formatting the figures of this paper.

The authors are grateful for the support received from the Global Commission on Evidence to Address Societal Challenges to conduct this work.

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CM and JNL discussed and created the idea. CM, AS, GG and JNL wrote the protocol. CM and JNL participated in the data collection, and everyone participated in the iterative data analysis. All authors read and approved the final manuscript.

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Mansilla, C., Sweetman, A., Guyatt, G. et al. A taxonomy of demand-driven questions for use by evidence producers, intermediaries and decision-makers: results from a cross-sectional survey. Health Res Policy Sys 22 , 78 (2024). https://doi.org/10.1186/s12961-024-01160-4

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