effects of global warming essay in marathi

ग्लोबल वॉर्मिंग चे दुष्परिणाम निबंध मराठी । The Effects of Global Warming Essay Marathi

आपल्या पृथ्वीवर अनेक समस्या बघायला मिळत आहेत. त्यातील सर्वात मोठ्या समस्या म्हणजे प्रदूषण आणि दुसरी म्हणजे ” ग्लोबल वार्मिंग” ( Global Warming).

ग्लोबल वार्मिंग म्हणजे पृथ्वीवरील तापमानात होणारी सातत्याने वाढ. आज पृथ्वीच्या पृष्ठभागावर दिवसें दिवस तापमान वाढत आहे. आणि आजच्या निबंधा मध्ये आपण हाच विषय सर्वांसमोर घेऊन येत आहोत तो म्हणजेच ” ग्लोबल वार्मिंग” .

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ग्लोबल वार्मिंग म्हणजे काय ? 

” ग्लोबल वार्मिंग” म्हणजे पृथ्वीच्या वातावरणीय तापमान अचंबितपणे होणारी वाढ. प्रदूषण हा ग्लोबल वॉर्मिंग वाढविण्या मागील सर्वात महत्त्वाचा घटक आहे.

जेव्हा पृथ्वीवरील सरासरी तापमान मर्यादीत पातळीपेक्षा जास्त वाढते तेव्हा ग्लोबल वार्मिंग ही समस्या उद्भवते. आणि याचा मुख्य परिणाम होतो म्हणजेच ग्रीनहाऊस वर आणि याला ” ग्रीनहाऊस” इफेक्ट म्हणून ओळखले जाते. पाण्याची वाफ, मिथेन आणि नायट्रोस ऑक्साईड हे काही प्रमुख ग्रीनहाउस वायू आहेत.

ग्रीनहाऊस इफेक्ट म्हणजे काय ?

जेव्हा आपण जीवाश्म इंधने वातावरणामध्ये प्रज्वलित करतो म्हणजे जाळतो. तेव्हा कार्बन डायऑक्साईड, मिथेन या सारखे वायू वातावरणात पसरतात.

ज्यामुळे सूर्या पासून येणारे घातक किरणे आणि अति उष्णता यांपासून आपले बचाव करणारे काही तर विरघळतात याला ” ग्रीनहाऊस इफेक्ट” असे म्हणतात . व परिणामी ग्रीनहाऊस च्या इफेक्ट मुळे पृथ्वीच्या तापमानात वाढ होते आणि या तापमान वाढीला ग्लोबल वार्मिंग असे म्हटले जाते. अशा प्रकारे पृथ्वीच्या तापमानात वाढ होते.

आणि पृथ्वीवरील हवामान आणि जैवविविधतेतील बर्‍याचश्या बदलांना ग्लोबल वार्मिंग कारणीभूत ठरते. ग्लोबल वार्मिंग चे परिणाम जगातील अंटार्टिका खंडाचा स्पष्टपणे दिसत आहेत. आपल्या आजूबाजूला ग्लोबल वार्मिंग मुळे अत्यंत दुष्काळाच्या घटना घडताना आपण बघत असतो.

वातावरणातील ऋतुचक्र बदलत चाले आहे. ज्या प्रदेशात पावसाची गरज भासते तेथे पाऊस न पडता अन्य भागात पडत आहे. अति उष्णतेमुळे हिमनद्या वितळत आहे.

त्यामुळे समुद्राची पातळीत वाढ होऊन परिणामी पूर येतो. तसेच वाढत्या ग्लोबल वार्मिंग मुळे जंगल प्रजातींवर परिणाम होत आहे. अतिरिक्त ग्लोबल वार्मिंग मुळे जंगलात लागणारी आग यामुळे जंगले नष्ट होऊन अनेक पक्षी आणि प्राणी नष्ट होत आहेत.

समुद्रातील जलचर प्राण्यांचे प्रमाण कमी होण्या मागे ग्लोबल वार्मिंग एकमेव कारण आहे. तापमानात होणाऱ्या बदलांसाठी कोरल रीफ्स अत्यंत महत्त्वाचे आणि संवेदनशील असतात. पण अलीकडे वाढत्या तापमाना मुळे रीफ्सी संख्या कमी होत आहे.

ग्लोबल वार्मिंग चे कारणे :

आज आपल्या सर्वांच्या जीवनासाठी ग्लोबल वॉर्मिंग हे खूप मोठी समस्या बनली आहे. यामुळे पृथ्वीवरील तापमान वाढून अनेक अडचणींना तोंड द्यावे लागत आहे. आणि हे ग्लोबल वार्मिंग वाढवण्याची काही मुख्य कारणे आहेत ती पुढीलप्रमाणे,

ग्लोबल वार्मिंग वाढवण्यासाठी जंगलतोड ही एक मुख्य कारण बनत आहे. झाडे ही नैसर्गिकरित्या हवा स्वच्छ करण्यासाठी महत्त्वाची ठरतात. झाडांमध्ये co2 गॅस शोषून घेण्याची क्षमता असते.

झाडे आपल्याला ऑक्सिजन पुरवितात व वातावरणातील कार्बन डायऑक्साईड वायू शोषून घेऊन वातावरण शुद्ध करतात. पृथ्वीवरील मानव प्राणी आणि सर्व सजीवांसाठी ऑक्सिजनची किती गरज आहे ही सांगण्याची आवश्यकता नाही.

पण आज औद्योगीकरणासाठी, शहरी करण्यासाठी आणि व्यवसायासाठी जंगले तोडली जात आहेत. त्यामुळे वातावरणात co2 गॅसचे प्रमाण वाढून ग्लोबल वार्मिंग चे प्रमाण वाढत आहे.

आपण जेवढे जास्त जंगले तोडून तेवढ्या मोठ्या प्रमाणात किंवा त्यापेक्षा अधिक प्रमाणात वातावरणात co2 गॅस पसरेल व हा गॅस ग्लोबल वॉर्मिंग वाढवण्यासाठी कारणीभूत ठरत आहे.

जीवाश्म इंधन जाळणे :

जेव्हा आपण वातावरणामध्ये जीवाश्म इंधन जाळतो तेव्हा ग्लोबल वार्मिंग चे प्रमाण वाढले जाते. मनुष्याने केलेल्या ग्रीनहाऊस वायूंचे उत्पादन विविध जीवाश्म इंधनांच्या निर्मिती व वापरासाठी केला जातो परिणामी ग्लोबल वार्मिंग होते.

वाहनामध्ये वापरले जाणारे इंधन म्हणजे पेट्रोल जळून जेव्हा वातावरणात धूर फेकला जातो तेव्हा त्यातून सी co2 गॅस बाहेर सोडला जातो. अनेक वाहने, व वीज निर्मिती प्रकल्पांमध्ये कोसळ वापरला जाते. व त्यामधून co2 वायू वातावरणात पसरतो व त्याचा परिणाम ग्लोबल वार्मिंग अशा समस्यांद्वारे उद्भवतो.

ग्लोबल वॉर्मिंग वाढण्यामागे सर्वात महत्त्वाचे कारण म्हणजे प्रदूषण आहे. वातावरणामध्ये होणारे वायू प्रदूषण, जल प्रदूषण आणि ध्वनी प्रदूषण यांचा परिणाम ग्लोबल वॉर्मिंग वर होत आहे.

वायु प्रदूषण आतून निघणारे घातक वायू तापमान वाढीसाठी कारणीभूत ठरत आहेत. आपण घरातून अथवा आपल्या परिसरातून निघणारा कचरा उघड्यावर जाळतो त्यातून निघणारा धूरातून ग्लोबल वार्मिंगच्या समस्यांमध्ये रुपांतर होत आहे.

घातक खते आणि रासायने :

कृषी क्षेत्र सुद्धा जागतिक तापमान वाढीस कारणीभूत ठरत आहे. आज कृषी उद्योग रासायनिक खते आणि जंतुनाशकांचा वापर मोठ्या प्रमाणात करत आहेत. त्यातील बहुतेक विघटना नंतर हानिकारक हरितग्रह वायू करतात.

रासायनिक खते आणि जंतुनाशके ग्रीन हाउस वायूंच्या पैकी NO2 हा वायू ( नायट्रस ऑक्साईड ) तयार करतात परिणामी ग्लोबल वार्मिंग वाढते. तसेच आपल्या सभोवती गाई, म्हशी आणि इतर जनावरे ( CH4 ) मिथेन वायू तयार करतात व त्यामुळे ही ग्लोबल वार्मिंग वाढते.

ग्लोबल वार्मिंग चे परिणाम :

आज आपल्या पृथ्वीवर ग्लोबल वार्मिंग चे महान संकट असल्याचे दिसत आहे. त्याचा परिणाम संपूर्ण जीवसृष्टी व पर्यावरणावर होत आहे. त्यातील काही परिणाम पुढील प्रमाणे आहेत.

तापमान वाढ :

ग्लोबल वार्मिंग मुळे पृथ्वीच्या पृष्ठभागाच्या तापमानात वाढ होत आहेत. संपूर्ण विसाव्या शतकात सर्वाधिक तापमान वाढ नोंदली गेली आहे. या तापमानाचे प्रमाण संपूर्ण जगात कमी- अधिक प्रमाणात आढळते. व या तापमान वाढीमुळे पावसाचे प्रमाण कमी होत आहे. आणि अचानक जंगलांमध्ये लागणाऱ्या आग यांमागे अति उष्ण तापमान हेच कारण आहे.

ग्लोबल वार्मिंग मुळे पृथ्वीवरील तापमानात वाढ झाली आहे. त्यामुळे अति उष्ण तापमानामुळे हिमनग ( बर्फाचे डोंगर ) हळूहळू वितळत आहे. अमेरिका आणि अशिया खंडात गेल्या 50 वर्षेत या हिमनग घटीमध्ये वाढ झाली आहे. उत्तर गोलार्धातील प्रदेशातील बर्फ वितळण्यामध्ये 10 टक्के वाढ झाले आहे. या ग्लोबल वॉर्निंग मुळे जगभरातील सर्व हिमनग वितळत आहेत.

समुद्र पातळीत वाढ :

अति उष्णते मुळे बर्फाचे डोंगर वितळत आहेत व ते पाणी समुद्राला जात आहे. परिणामी समुद्र पातळी वाढत आहे. गेल्या काही वर्षा पासून असे लक्षात आले की, समुद्रातील पातळी सुमारे 8 इंच ने वाढली आहे. अंटार्टिका मध्ये मोठ्या प्रमाणात हिमनद्या वितळत आहेत.

महासागर अम्लीकरण :

ग्रीनहाऊस इफेक्ट मुळे वातावरणात वाढणारे कार्बन-डाय-ऑक्‍साईड ( co2 ) च्या पातळीमध्ये वाढ होत आहे. co2 गॅस हा समुद्राच्या पाण्यात विरघळतो व समुद्राच्या पाण्यात अम्लात येते. आणि या अम्लाता मध्ये जलचर प्राणी जास्त काळ राहू शकत नाही. त्यामुळे समुद्रातील अनेक प्रजाती नष्ट होत आहेत.

ग्लोबल वार्मिंग थांबवण्यासाठी काय करावे :

मुख्यता ग्लोबल वार्मिंग वाढण्यामागे प्रदूषण हे महत्त्वाचे कारण आहे. मग आपण प्रदूषण कमी करण्यासाठी प्रयत्न करू. ग्रीनहाऊस वायू ज्या पदार्थां मधून बाहेर पडतात ते पदार्थ आपण उघड्यावर न जाळता त्यांचा वापर कमी प्रमाणात करावा. असे केल्यास ग्लोबल वार्मिंग कमी होण्यामध्ये मदत होईल.

तसेच वृक्षतोड ही सुद्धा ग्लोबल वार्मिंग वाढविण्यास कारणीभूत आहे. म्हणून आपण वृक्षतोड कमी करून ग्लोबल वार्मिंग थांबवण्यासाठी हातभार लावावा.

कारखाने आणि औद्योगिकरणा मधून निघणाऱ्या घातक वायूं मुळे ग्लोबल वार्मिंग वाढत आहे. मग हे थांबविणे आपले कर्तव्य आहे. रासायनिक खते आणि जंतु नाशकामुळे घातक वायू बाहेर पडतात मग आपण या रासायनिक खतांचा वापर कमी करून सेंद्रिय खते वापरावी.

वाहनांमध्ये वापरणारे पेट्रोल-डिझेल आणि वीज निर्मिती प्रकल्पांमध्ये वापरणारा कोळसा ह्या जीवाश्म इंधनाचा वापर कमी करावा. जीवाश्म इंधनामधून घातक co2 सारखे वायू बाहेर पडतात व त्यामुळे ग्लोबल वॉर्मिंग वाढते म्हणून या जीवाश्म इंधनांचा वापर गरजेपुरता व मर्यादितच करावा.

ग्लोबल वार्मिंग ही मुख्यतः मानवी कार्यामुळे वाढत आहे. औद्योगीकरण आणि वाहतूक ही कारणे ग्लोबल वार्मिंग वाढविण्यासाठी कारणीभूत ठरत आहेत.

ग्लोबल वार्मिंग कमी करण्याचा एकमेव मार्ग म्हणजे ग्रीनहाउस वायूंचे उत्पादन कमी करणे आहे. जीवाश्म इंधना ऐवजी इतर इंधन स्त्रोत्र वापरणे फायदेशीर ठरेल.

ग्लोबल वार्मिंग कमी करण्याची जबाबदारी ही मानवावर आहे. ही समस्या एक जागतिक समस्या झाली आहे. म्हणून ग्लोबल वार्मिंग कमी करण्यासाठी जागतिक समुदायाने कठोर पावले उचलणे गरजेचे आहेत.

ये निबंध देखील अवश्य वाचा :-

• वाचाल तर वाचाल मराठी निबंध
• मोबाईल फोनचे फायदे आणि तोटे मराठी निबंध
• सोशल मीडिया वर निबंध मराठी
• गाय वर मराठी निबंध
• रक्षा बंधन माहिती मराठी

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ग्लोबल वॉर्मिंग निबंध मराठी Global Warming Essay in Marathi

Global Warming Essay in Marathi – Global Warming Che Dushparinam Essay in Marathi ग्लोबल वॉर्मिंग निबंध मराठी मित्रहो, आपणा सर्वांना तापमानवाढीच्या संदर्भात दूरदर्शनवर पाहून किंवा वर्तमानपत्रात अथवा मासिकांमध्ये वाचून माहिती झालं असेल की गेल्या शंभर वर्षांमध्ये यापूर्वी कधीही झालेली नाही, एवढ्या झपाट्यानं अनेक वेळा तापमानवाढ झालेली आहे. खरंतर, विषुववृत्तीय प्रदेशांमधील काही पर्वत शिखरे ही हिमाच्छादित आहेत. यांतील, किलिमांजारो हे पर्वत शिखर खूप प्रसिद्ध आहे. परंतू, या पर्वत शिखरावरील हिमाच्छादन इसवी सन १९०६ च्या तुलनेत केवळ २५ टक्के एवढेच उरले आहे.

शिवाय, आल्पस् आणि हिमालयातील बहुतांश हिमनद्या या मागे हटत चालल्या आहेत. त्यामुळे, हिमरेषा म्हणजे ज्या ऊंचीपर्यंत कायम हिमाच्छादन असते किंवा आजच्या भाषेत जिथे २४ गुणिले ७ इतके हिमाच्छादन असते, ती रेषा आज वरच्या दिशेने सरकत चालली आहे. एव्हरेस्टवर जाताना लागणारी खुंबू हिमनदी ही इसवी सन १९५३ ते इसवी सन २००३ या ५० वर्षांमध्ये जवळजवळ पाच किलोमीटर मागे सरकली आहे.

याखेरीज, इसवी सन १९७० च्या मध्यापासून नेपाळमधील सरासरी तापमान १० से. ने वाढले आहे, तर सैबेरियातील कायमस्वरूपी असलेल्या हिमाच्छादित प्रदेशात गेल्या ३० वर्षांत म्हणजे इसवी सन १९७५ ते ७६ पासून १.५ से. तापमानवाढ नोंदवण्यात आली आहे. शिवाय, येथील हिमाच्छादन दरवर्षी २० सें. मी. चा थर मागे टाकून देत आहे.

ग्लोबल वॉर्मिंग निबंध मराठी – Global Warming Essay in Marathi

Global warming che dushparinam essay in marathi.

मित्रांनो, अशी विविध प्रकारची जागतिक तापमानवाढीची उदाहरणे आपल्याला दिसून येतं आहेत. सागरपृष्ठावरील तापमानवाढ ही, या सागरी तुफानांना जबाबदार असतेच, परंतू अनेक वेळा  सागरांतर्गत होणारी तापमानवाढ देखील या तुफानांची तीव्रता आणि संहारक शक्ती वाढवत असते.

याचं उत्तम उदाहरण म्हणजे; रिटा आणि कॅटरिना या संहारक तुफानांनंतर जो अभ्यास करण्यात आला, त्यामध्ये  मेक्सिकोच्या आखातातील खोलवर असलेल्या उबदाफेलिक्स या सागरी तुफानाच्या वेळी त्या भागावरच्या आकाशात कार्बन डायऑक्साईडची पातळी साधारणतः १०० पटींनी वाढल्याचं दिसून आलं होतं.

तेंव्हापासून, लिहून ठेवलेल्या अनेक नोंदी या सागरी तुफानांची ही बाजू व्यवस्थितरीत्या स्पष्ट करण्यासाठी पुरेशा आहेत. असे असले तरीदेखील  मित्रांनो, सागरी तुफानांच्या तीव्रतेचा संबंध वाढत्या जागतिक तापमानाशी लावायला मात्र शास्त्रज्ञांचा एकही गट तयार नाही.

कारण, इसवी सन १९७० पूर्वी घेतलेली सागर तुफानांची ही मोजमापं, आताच्या उपग्रहांनी घेतलेल्या मोजमापांइतकी अचूक नाहीत असे म्हणणे या शास्त्रज्ञांच्या गटाचे आहे.

हरितगृह परिणाम

खरंतर, हरितगृह हे खास प्रकारच्या आणि बहुतांश प्रजातींच्या वनस्पती वाढवण्यासाठी बनवलेले काचेचे घर असते. वनस्पतींवर बाह्य हवामानाचा अथवा विपरित वातावरणाचा वाईट परिणाम होऊ नये, यांसाठी असे घर बांधण्यात येते. परंतू, सूर्यापासून निघणाऱ्या अवरक्त लहरी या हरितगृहातील विशिष्ठ वायूंमुळे पुन्हा पृथ्वीच्या वातावरणामध्ये परावर्तित होतात, त्यामुळे रात्रीच्या वेळीही पृथ्वीला ऊर्जा मिळते.

एकंदरीत, वातावरणात अडकून राहिलेल्या या अवरक्त लहरींच्या ऊर्जेमुळे पृथ्वीभोवतालचे वातावरण उबदार राहण्यास मदत होते. जर हे वायु वातावरणात अडकून राहिले नसते, तर पृथ्वीचे तापमान रात्रीच्या वेळी भारतासारख्या उबदार देशातही -१८ अंश सेल्सियस इतके असते.

मित्रहो, आता तुम्हीच सांगा जर आपल्या भारतासारख्या ठिकाणी अशी परिस्थिती उद्भवणार असेल, तर रशिया आणि कॅनडा यांसारख्या देशांमध्ये किती बिकट परिस्थिती उद्भवेल.

कारण, यांसारख्या देशांमध्ये आधीच कमी तापमान असते. परंतू मित्रांनो, हरितगृहातील वायूंमुळे रात्रीचे तापमान हे थोड्याशा प्रमाणामध्ये देखील कमी होत नाही. त्यामुळे, आपल्या पृथ्वीचे सरासरी तापमान -१८ पेक्षा ३३° जास्त म्हणजे १५° सेल्सियस इतके राहते. शिवाय, या परिणामामुळे मुख्यत्वे पृथ्वीवरील जीवसृष्टी ही विकसित झालेली आहे.

हरितगृह हे काचेचे घर असून, या घरामध्ये ऊन येण्यास व्यवस्था असते. परंतू दुसरीकडे, हे घर बंदिस्त असल्या कारणाने, हे घर उन्हाने तापल्यानंतर या घरातील आतील तापमान कमी होण्यास मज्जाव असतो.

अशा प्रकारे, घराच्या आतील भागातील तापमान उबदार राहण्याच्या संकल्पनेमुळे ही संज्ञा जागतिक तापमान वाढीच्या संदर्भात वापरली जाते. याखेरीज, काही वायूंच्या रेणूंची रचना अशा प्रकारची असते की ते उष्णतेच्या ऊर्जालहरी सहजपणे परावर्तित करू शकतात.

यामध्ये, कार्बन डायऑक्साइड, मिथेन, डायनायट्रोजन ऑक्साईड आणि पाण्याची वाफ असे जे प्रमुख वायु आहेत, हे वायू या उर्जालहरी सहजरीत्या परावर्तित करू शकतात. आपल्याला माहीत आहे की, सूर्यापासून पृथ्वीला मिळणाऱ्या ऊर्जेमध्ये अतिनील ते अवरक्त अशा प्रकारच्या ऊर्जालहरींचा समावेश असतो, उदाहरणार्थ; अवरक्त किंवा इन्फ्रारेड ऊर्जा लहरी म्हणजे उष्णतेच्या लहरी.

याखेरीज, पृथ्वीवर येणाऱ्या सूर्यप्रकाशातील सर्व ऊर्जालहरी या बऱ्यापैकी दिवसाच्या वेळेला भुपृष्ठावर शोषल्या जातात. त्यामुळे, पृथ्वीवर दिवसाच्या वेळी तापमान वाढलेले असते. तर दुसरीकडे, सूर्य मावळल्यानंतर ही शोषण प्रक्रिया थांबते व उत्सर्जन प्रक्रिया सूरु होते. अशा प्रकारे, शोषलेल्या लहरी या अंतराळात पुन्हा सोडल्या जातात.

तापमानवाढीची कारणे

जगाची वाढती लोकसंख्या.

आजकाल, जगाच्या वाढत्या लोकसंख्येमुळे, कार्बन-डाय-ऑक्साइड या वायूच्या उत्सर्जनाचे प्रमाण भरपूर वाढत आहे.

प्राण्यांची वाढती संख्या

मित्रहो, कार्बन-डाय-ऑक्साइड या वायूचे  प्रमाण वाढण्याकरिता जबाबदार ठरलेले आणखी एक महत्वपूर्ण कारण म्हणजे जगात वाढणारी प्राण्यांची प्रचंड संख्या. त्यामुळे, जगातील अनेक देशांमध्ये प्राणी सांभाळण्याबाबत अनेक कडक नियम लागू केलेले  आहेत आणि याचे उत्तम उदाहरण म्हणजे; अमेरिकेमध्ये कडक नियम आणि कायदे टाळण्याकरिता तेथील वराहपालक मेक्सिकोमध्ये वराहपालन केंद्रे काढतात.

शिवाय, याठिकाणी एकेका केंद्रावर कितीतरी लाख प्राणी असतात. त्याचबरोबर, अमेरिकेतील कॅलिफोर्निया या राज्यामध्ये दशलक्षावधी गाई आहेत. न्यूझीलंडमध्ये तर देशाच्या लोकसंख्येच्या अनेकपट मेंढ्याच आहेत. याखेरीज, आपल्याला जगातील कोंबड्यांची तर गणतीच करता येणार नाही.

मित्रहो, आता तुम्हाला प्रश्न पडला असेल की प्राण्यांचा तापमानवाढीशी काय संबंध? तर मित्रांनो, हे सर्व प्राणी श्वासावाटे ऑक्सिजन घेतात आणि कार्बन-डाय-ऑक्साइड बाहेर टाकतात.

शिवाय, या प्राण्यांच्या  मलमार्गावाटे मिथेन हा घातक हरितगृह परिणाम घडवून आणणारा वायू बाहेर टाकला जातो आणि आपणा सर्वांना माहीत आहे की मिथेन वायू हा कार्बन-डाय-ऑक्साइडपेक्षा अनेक पट घातक हरितगृह परिणाम घडवून आणणारा वायू आहे. त्यामुळे, प्राण्यांची संख्या आटोक्यात आणणे गरजेचे आहे.

सूर्याकिरणांची दाहकता

तापमानवाढीच्या संदर्भातील तिसरे महत्वाचे कारण म्हणजे सूर्यकिरणांची दाहकता. कारण, दाहकता वाढल्यास जागतिक तापमानात वाढ होण्याची भरपूर शक्यता असते. परंतू, सध्याच्या परिस्थितीत सूर्य किरणांचे उत्सर्जन हे नेहमीप्रमाणे होत आहे. अशा प्रकारे, किरणांची दाहकता कमी-जास्त झाल्यास, जागतिक तापमान देखील त्यानुसार  कमी-जास्त होते. 

तापमानवाढ रोखण्यासाठी उपाय

जागतिक तापमानवाढ रोखायची असेल, तर सर्वात पहिल्यांदा आपल्याला वातावरणातील कार्बन डायऑक्साईड वायू कमी करण्यासाठी उपाय करावे लागतील. यासाठीचा एक उत्तम आणि सोपा उपाय म्हणजे झाडे वाढवणे. सध्याचे कार्बन डायऑक्साईड या वायूचे वातावरणातील प्रमाण थोपवायचे असेल, तर झाडांची संख्या वाढवणे खूप गरजेचे आहे.

कारण, जंगलांखालची भूमी ही सध्याच्या जंगलांखालच्या भूमिपेक्षा तीन ते पाच पटीने वाढवायला हवी. दुसरे म्हणजे कार्बन डायऑक्साईड निर्माण होताच, हा वायू पकडून समुद्रात सोडायची सुविधा करायला हवी अथवा याचे दुसऱ्या एखाद्या अविघटनशील संयुगात रूपांतर करायला हवे.

कारण, सागरात मोठ्या प्रमाणावर लोहसंयुगे ओतली, तर वानस प्लवकांची वाढ होते आणि त्यामुळे कार्बन डायऑक्साईडचे प्रमाण कमी व्हायला मदत देखील होते, असे काही  शास्त्रज्ञांचे मत आहे.

खरंतर, सध्याच्या धावपळीच्या युगात कोणताही देश उर्जेचा वापर कमी करून आपली प्रगती खोळंबून घेणार नाही. त्यामुळे, अभ्यासातील पाहणीनुसार असे लक्षात येते की विकसित देशांचा उ‍र्जेचा वापर हा विकसनशील देशांपेक्षा कितीतरी पटीने अधिक आहे. परंतू, त्यांच्या वापराचे प्रमाण मात्र स्थिरावले आहे.

कारण, या देशांसमोर देखील हाच  मोठा प्रश्न उभा आहे की ऊर्जेचा वापर कमी कसा करायचा? जेणेकरुन, हरितवायूंचे प्रमाण कमी होईल आणि जागतिक तापमानात घट होईल. शेवटी मित्रांनो, झाडांची लागवड करणे आणि त्यांची जोपासना करणे, हेच आपल्यासाठी जास्त सोपे व सोयीस्कर आहे. त्यामुळे, याबाबत सगळ्यांनी गंभीरतेने विचार करून, झाडांची लागवड केली पाहिजे. कारण, तेंव्हाच कुठंतरी आपणा सर्वांना भविष्यामध्ये तापमानवाढीला आटोक्यात आणता येईल.

                      तेजल तानाजी पाटील

                         बागीलगे, चंदगड.

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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Glanemann, N., Willner, S. N. & Levermann, A. Paris Climate Agreement passes the cost-benefit test. Nat. Commun. 11 , 110 (2020).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527 , 235–239 (2015).

Article   ADS   CAS   PubMed   Google Scholar  

Kalkuhl, M. & Wenz, L. The impact of climate conditions on economic production. Evidence from a global panel of regions. J. Environ. Econ. Manag. 103 , 102360 (2020).

Article   Google Scholar  

Moore, F. C. & Diaz, D. B. Temperature impacts on economic growth warrant stringent mitigation policy. Nat. Clim. Change 5 , 127–131 (2015).

Article   ADS   Google Scholar  

Drouet, L., Bosetti, V. & Tavoni, M. Net economic benefits of well-below 2°C scenarios and associated uncertainties. Oxf. Open Clim. Change 2 , kgac003 (2022).

Ueckerdt, F. et al. The economically optimal warming limit of the planet. Earth Syst. Dyn. 10 , 741–763 (2019).

Kotz, M., Wenz, L., Stechemesser, A., Kalkuhl, M. & Levermann, A. Day-to-day temperature variability reduces economic growth. Nat. Clim. Change 11 , 319–325 (2021).

Kotz, M., Levermann, A. & Wenz, L. The effect of rainfall changes on economic production. Nature 601 , 223–227 (2022).

Kousky, C. Informing climate adaptation: a review of the economic costs of natural disasters. Energy Econ. 46 , 576–592 (2014).

Harlan, S. L. et al. in Climate Change and Society: Sociological Perspectives (eds Dunlap, R. E. & Brulle, R. J.) 127–163 (Oxford Univ. Press, 2015).

Bolton, P. et al. The Green Swan (BIS Books, 2020).

Alogoskoufis, S. et al. ECB Economy-wide Climate Stress Test: Methodology and Results European Central Bank, 2021).

Weber, E. U. What shapes perceptions of climate change? Wiley Interdiscip. Rev. Clim. Change 1 , 332–342 (2010).

Markowitz, E. M. & Shariff, A. F. Climate change and moral judgement. Nat. Clim. Change 2 , 243–247 (2012).

Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42 , 153–168 (2017).

Auffhammer, M., Hsiang, S. M., Schlenker, W. & Sobel, A. Using weather data and climate model output in economic analyses of climate change. Rev. Environ. Econ. Policy 7 , 181–198 (2013).

Kolstad, C. D. & Moore, F. C. Estimating the economic impacts of climate change using weather observations. Rev. Environ. Econ. Policy 14 , 1–24 (2020).

Dell, M., Jones, B. F. & Olken, B. A. Temperature shocks and economic growth: evidence from the last half century. Am. Econ. J. Macroecon. 4 , 66–95 (2012).

Newell, R. G., Prest, B. C. & Sexton, S. E. The GDP-temperature relationship: implications for climate change damages. J. Environ. Econ. Manag. 108 , 102445 (2021).

Kikstra, J. S. et al. The social cost of carbon dioxide under climate-economy feedbacks and temperature variability. Environ. Res. Lett. 16 , 094037 (2021).

Article   ADS   CAS   Google Scholar  

Bastien-Olvera, B. & Moore, F. Persistent effect of temperature on GDP identified from lower frequency temperature variability. Environ. Res. Lett. 17 , 084038 (2022).

Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9 , 1937–1958 (2016).

Byers, E. et al. AR6 scenarios database. Zenodo https://zenodo.org/records/7197970 (2022).

Burke, M., Davis, W. M. & Diffenbaugh, N. S. Large potential reduction in economic damages under UN mitigation targets. Nature 557 , 549–553 (2018).

Kotz, M., Wenz, L. & Levermann, A. Footprint of greenhouse forcing in daily temperature variability. Proc. Natl Acad. Sci. 118 , e2103294118 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Myhre, G. et al. Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci. Rep. 9 , 16063 (2019).

Min, S.-K., Zhang, X., Zwiers, F. W. & Hegerl, G. C. Human contribution to more-intense precipitation extremes. Nature 470 , 378–381 (2011).

England, M. R., Eisenman, I., Lutsko, N. J. & Wagner, T. J. The recent emergence of Arctic Amplification. Geophys. Res. Lett. 48 , e2021GL094086 (2021).

Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5 , 560–564 (2015).

Pfahl, S., O’Gorman, P. A. & Fischer, E. M. Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Clim. Change 7 , 423–427 (2017).

Callahan, C. W. & Mankin, J. S. Globally unequal effect of extreme heat on economic growth. Sci. Adv. 8 , eadd3726 (2022).

Diffenbaugh, N. S. & Burke, M. Global warming has increased global economic inequality. Proc. Natl Acad. Sci. 116 , 9808–9813 (2019).

Callahan, C. W. & Mankin, J. S. National attribution of historical climate damages. Clim. Change 172 , 40 (2022).

Burke, M. & Tanutama, V. Climatic constraints on aggregate economic output. National Bureau of Economic Research, Working Paper 25779. https://doi.org/10.3386/w25779 (2019).

Kahn, M. E. et al. Long-term macroeconomic effects of climate change: a cross-country analysis. Energy Econ. 104 , 105624 (2021).

Desmet, K. et al. Evaluating the economic cost of coastal flooding. National Bureau of Economic Research, Working Paper 24918. https://doi.org/10.3386/w24918 (2018).

Hsiang, S. M. & Jina, A. S. The causal effect of environmental catastrophe on long-run economic growth: evidence from 6,700 cyclones. National Bureau of Economic Research, Working Paper 20352. https://doi.org/10.3386/w2035 (2014).

Ritchie, P. D. et al. Shifts in national land use and food production in Great Britain after a climate tipping point. Nat. Food 1 , 76–83 (2020).

Dietz, S., Rising, J., Stoerk, T. & Wagner, G. Economic impacts of tipping points in the climate system. Proc. Natl Acad. Sci. 118 , e2103081118 (2021).

Bastien-Olvera, B. A. & Moore, F. C. Use and non-use value of nature and the social cost of carbon. Nat. Sustain. 4 , 101–108 (2021).

Carleton, T. et al. Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits. Q. J. Econ. 137 , 2037–2105 (2022).

Bastien-Olvera, B. A. et al. Unequal climate impacts on global values of natural capital. Nature 625 , 722–727 (2024).

Malik, A. et al. Impacts of climate change and extreme weather on food supply chains cascade across sectors and regions in Australia. Nat. Food 3 , 631–643 (2022).

Article   ADS   PubMed   Google Scholar  

Kuhla, K., Willner, S. N., Otto, C., Geiger, T. & Levermann, A. Ripple resonance amplifies economic welfare loss from weather extremes. Environ. Res. Lett. 16 , 114010 (2021).

Schleypen, J. R., Mistry, M. N., Saeed, F. & Dasgupta, S. Sharing the burden: quantifying climate change spillovers in the European Union under the Paris Agreement. Spat. Econ. Anal. 17 , 67–82 (2022).

Dasgupta, S., Bosello, F., De Cian, E. & Mistry, M. Global temperature effects on economic activity and equity: a spatial analysis. European Institute on Economics and the Environment, Working Paper 22-1 (2022).

Neal, T. The importance of external weather effects in projecting the macroeconomic impacts of climate change. UNSW Economics Working Paper 2023-09 (2023).

Deryugina, T. & Hsiang, S. M. Does the environment still matter? Daily temperature and income in the United States. National Bureau of Economic Research, Working Paper 20750. https://doi.org/10.3386/w20750 (2014).

Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146 , 1999–2049 (2020).

Cucchi, M. et al. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12 , 2097–2120 (2020).

Adler, R. et al. The New Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly Analysis Product 1072–1084 (University of Maryland, 2016).

Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12 , 3055–3070 (2019).

Wenz, L., Carr, R. D., Kögel, N., Kotz, M. & Kalkuhl, M. DOSE – global data set of reported sub-national economic output. Sci. Data 10 , 425 (2023).

Article   PubMed   PubMed Central   Google Scholar  

Gennaioli, N., La Porta, R., Lopez De Silanes, F. & Shleifer, A. Growth in regions. J. Econ. Growth 19 , 259–309 (2014).

Board of Governors of the Federal Reserve System (US). U.S. dollars to euro spot exchange rate. https://fred.stlouisfed.org/series/AEXUSEU (2022).

World Bank. GDP deflator. https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS (2022).

Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11 , 084003 (2016).

Murakami, D. & Yamagata, Y. Estimation of gridded population and GDP scenarios with spatially explicit statistical downscaling. Sustainability 11 , 2106 (2019).

Koch, J. & Leimbach, M. Update of SSP GDP projections: capturing recent changes in national accounting, PPP conversion and Covid 19 impacts. Ecol. Econ. 206 (2023).

Carleton, T. A. & Hsiang, S. M. Social and economic impacts of climate. Science 353 , aad9837 (2016).

Article   PubMed   Google Scholar  

Bergé, L. Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm. DEM Discussion Paper Series 18-13 (2018).

Kalkuhl, M., Kotz, M. & Wenz, L. DOSE - The MCC-PIK Database Of Subnational Economic output. Zenodo https://zenodo.org/doi/10.5281/zenodo.4681305 (2021).

Kotz, M., Wenz, L. & Levermann, A. Data and code for “The economic commitment of climate change”. Zenodo https://zenodo.org/doi/10.5281/zenodo.10562951 (2024).

Dasgupta, S. et al. Effects of climate change on combined labour productivity and supply: an empirical, multi-model study. Lancet Planet. Health 5 , e455–e465 (2021).

Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3 , 497–501 (2013).

Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. 114 , 9326–9331 (2017).

Wheeler, T. R., Craufurd, P. Q., Ellis, R. H., Porter, J. R. & Prasad, P. V. Temperature variability and the yield of annual crops. Agric. Ecosyst. Environ. 82 , 159–167 (2000).

Rowhani, P., Lobell, D. B., Linderman, M. & Ramankutty, N. Climate variability and crop production in Tanzania. Agric. For. Meteorol. 151 , 449–460 (2011).

Ceglar, A., Toreti, A., Lecerf, R., Van der Velde, M. & Dentener, F. Impact of meteorological drivers on regional inter-annual crop yield variability in France. Agric. For. Meteorol. 216 , 58–67 (2016).

Shi, L., Kloog, I., Zanobetti, A., Liu, P. & Schwartz, J. D. Impacts of temperature and its variability on mortality in New England. Nat. Clim. Change 5 , 988–991 (2015).

Xue, T., Zhu, T., Zheng, Y. & Zhang, Q. Declines in mental health associated with air pollution and temperature variability in China. Nat. Commun. 10 , 2165 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Liang, X.-Z. et al. Determining climate effects on US total agricultural productivity. Proc. Natl Acad. Sci. 114 , E2285–E2292 (2017).

Desbureaux, S. & Rodella, A.-S. Drought in the city: the economic impact of water scarcity in Latin American metropolitan areas. World Dev. 114 , 13–27 (2019).

Damania, R. The economics of water scarcity and variability. Oxf. Rev. Econ. Policy 36 , 24–44 (2020).

Davenport, F. V., Burke, M. & Diffenbaugh, N. S. Contribution of historical precipitation change to US flood damages. Proc. Natl Acad. Sci. 118 , e2017524118 (2021).

Dave, R., Subramanian, S. S. & Bhatia, U. Extreme precipitation induced concurrent events trigger prolonged disruptions in regional road networks. Environ. Res. Lett. 16 , 104050 (2021).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Maximilian Kotz, Anders Levermann & Leonie Wenz

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Contributions

All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Correspondence to Leonie Wenz .

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Nature thanks Xin-Zhong Liang, Chad Thackeray and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Essay on Global Warming – Causes and Solutions

500+ words essay on global warming.

Global Warming is a term almost everyone is familiar with. But, its meaning is still not clear to most of us. So, Global warming refers to the gradual rise in the overall temperature of the atmosphere of the Earth. There are various activities taking place which have been increasing the temperature gradually. Global warming is melting our ice glaciers rapidly. This is extremely harmful to the earth as well as humans. It is quite challenging to control global warming; however, it is not unmanageable. The first step in solving any problem is identifying the cause of the problem. Therefore, we need to first understand the causes of global warming that will help us proceed further in solving it. In this essay on Global Warming, we will see the causes and solutions of Global Warming.

Causes of Global Warming

Global warming has become a grave problem which needs undivided attention. It is not happening because of a single cause but several causes. These causes are both natural as well as manmade. The natural causes include the release of greenhouses gases which are not able to escape from earth, causing the temperature to increase.

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Further, volcanic eruptions are also responsible for global warming. That is to say, these eruptions release tons of carbon dioxide which contributes to global warming. Similarly, methane is also one big issue responsible for global warming.

So, when one of the biggest sources of absorption of carbon dioxide will only disappear, there will be nothing left to regulate the gas. Thus, it will result in global warming. Steps must be taken immediately to stop global warming and make the earth better again.

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Global Warming Solutions

As stated earlier, it might be challenging but it is not entirely impossible. Global warming can be stopped when combined efforts are put in. For that, individuals and governments, both have to take steps towards achieving it. We must begin with the reduction of greenhouse gas.

Furthermore, they need to monitor the consumption of gasoline. Switch to a hybrid car and reduce the release of carbon dioxide. Moreover, citizens can choose public transport or carpool together. Subsequently, recycling must also be encouraged.

Read Global Warming Speech here

For instance, when you go shopping, carry your own cloth bag. Another step you can take is to limit the use of electricity which will prevent the release of carbon dioxide. On the government’s part, they must regulate industrial waste and ban them from emitting harmful gases in the air. Deforestation must be stopped immediately and planting of trees must be encouraged.

In short, all of us must realize the fact that our earth is not well. It needs to treatment and we can help it heal. The present generation must take up the responsibility of stopping global warming in order to prevent the suffering of future generations. Therefore, every little step, no matter how small carries a lot of weight and is quite significant in stopping global warming.

हिंदी में ग्लोबल वार्मिंग पर निबंध यहाँ पढ़ें

FAQs on Global Warming

Q.1 List the causes of Global Warming.

A.1 There are various causes of global warming both natural and manmade. The natural one includes a greenhouse gas, volcanic eruption, methane gas and more. Next up, manmade causes are deforestation, mining, cattle rearing, fossil fuel burning and more.

Q.2 How can one stop Global Warming?

A.2 Global warming can be stopped by a joint effort by the individuals and the government. Deforestation must be banned and trees should be planted more. The use of automobiles must be limited and recycling must be encouraged.

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Essay on Causes and Effects of Global Warming for Students

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Causes and Effects of Global Warming:

Global Warming is caused by the increase in green house effect, resulting in a raised average earth’s temperature. The main causes of global warming is the release of toxic and harmful green house gases, like carbon dioxide and sulphur dioxide due to the burning of fossil fuel and industrial activities. Effects of global warming include incessant rain, unexpected climatic changes, rise in oceanic temperature, depletion in flora and fauna, melting of glaciers and floods etc.

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The essays have been written in simple yet effective English language to let you know about the causes and effects of global warming.

After going through these causes and effects of global warming essay you will know what are the causes of global warming and its effects; also the factors leading to it and steps taken to stop it.

The essays will be useful in your school/college assignments and speech giving, debate and essay writing competition.

Global Warming Causes and Effects Essay 1 (100 words)

Global warming is a major concern nowadays because of the regular increasing temperature of the whole atmosphere. It is continuously becoming so powerful like a demon. There are many reasons for its ever increasing nature. The major cause of it is greenhouse gases like carbon dioxide, nitrous oxide, methane, chloro fluoro carbons, etc., which are increasing in the environment mainly by the human activities. Some of are increasing number of automobiles, mushrooming industries, fossil fuels combustion, etc. Such activities emit more CO2 in the atmosphere which enhances global warmth of Earth. This increasing atmospheric temperature causes glaciers to melt, health disorders to occur and invites so many natural disasters.

Global Warming Causes and Effects Essay 2 (150 words)

As every one of us knows that couple of century, the climate on this planet was colder than now. And it is so surprising that even now it is not stable, it is increasing day by day. We are well aware and imagine its negative effects on our environment and body health. However, we are ignoring and continuing our bad habits which is flaming the global warming. Global warming is a constant process of increase in the annual temperature of whole planet. According to the statistics, it is recorded that in the last century the average increase in temperature is 0.7°C and sea level by 10 cm.

All the effects of the global warming (adverse effect on agriculture, floods, droughts, soil erosion, hurricanes, etc) are the huge indication of danger to our life. The main causes of the global warming are increasing human population, deforestation, increasing effects of green house gases, many careless human activities, etc.

Global Warming Causes and Effects Essay 3 (200 words)

There are many causes of the global warming which affects human lives and health in many aspects. The main cause of global warming is the human beings careless activities. Ever increasing human population are demanding more resources to live easy and healthy life such as land to live and crop cultivation, technological growth for comfort and deal with increasing temperature of atmosphere, etc. Fossil fuels burning for many reasons are highly responsible to increase global warming. People are realizing well the increase in environmental warmth however never mind their activities which are stimulating the global warming to increase. Continuously they are wasting natural resources and polluting the air, water and soil.

Increasing inventions of the technologies such as refrigerator, AC, car/automobiles, electric machines, oven, microwave, washing machine, etc., because of the increasing need of human beings for technologies in order to make their life, easy and luxurious. All such resources ultimately contribute to the global warming. Burning fossil fuels in the automobiles emits various green house gases which get collected in the atmosphere and force it to heat. Increasing need of land by human being results deforestation which contributes to increase in CO2 and decrease in oxygen. It affects the human health, life style, calls diseases, calls natural disasters like hurricanes, heat waves, drought, climate change and so many.

Global Warming Causes and Effects Essay 4 (250 words)

Increase in environmental temperature and climate change has become very clear over the last 50 years because of increasing level of greenhouse gases concentration (like water vapour, CO2, methane, ozone, sulphur and nitrogen gases, etc.) in the atmosphere. Such as greenhouse gases contribute to increase in the greenhouse effect.

The major cause of increasing greenhouse gas in the environment is burning of fossil fuels which emit carbon dioxide and heat the atmosphere. Such green house gases have capacity to absorb more heat from sun, heat from electricity used by human beings in many ways which in turn warm the whole atmosphere of this planet. The effects caused by greenhouse gases are called as the greenhouse effect.

There are some other dangerous effects of the global warming which continuously affects the lives of human beings, plants and animals. Global warming needs to be solved urgently by the active effort of each and every human being living on this planet.

Global Warming Causes and Effects Essay 5 (300 words)

Scientists of many countries are working regularly in field of global warming from many decades in order to figure out causes, effects, preventions measures, and solutions of it. In the last few decades, a huge level of change in the climate and weather has been seen clearly. It has disturbed the natural cycles and ecological balance in the atmosphere.

The most important cause of increasing global warming is the effect of greenhouse gases which emitted by human activities. In order to work together and solve this issue by global effort, there is group of scientists formed by United Nations . This group works together to research the various causes, dangerous effects, effective prevention measures, and effective solutions.

According to research it is seen that ever increasing level of several greenhouse gases are highly responsible for warming of atmosphere. Combustion of fossil fuels by various means such as automobiles, cars, factories, electricity use, etc. These are responsible for emitting various greenhouse gases like carbon dioxide, gases of sulphur and nitrogen, etc. Other contributors of the greenhouse effect are methane, nitrous oxide from various toxic fertilizers, loss of forests, etc.

Release of various toxic greenhouse gases in atmosphere is increasing atmospheric temperature as they have heat trapping abilities.

Methane gas molecules (20 times) and Nitrous oxide (300 times) have more ability to trap heat than CO2. Many countries use of chlorofluorocarbons has banned because they also have ability to degrade ozone layer as well as high potential to trap heat thousands of times more than CO2. Other causes of global warming is deforestation which increases CO2 level, changes climate and changes weather patterns.

Global Warming Causes and Effects Essay 6 (400 words)

Global warming is increasing day by day because of the continuous increase in the levels of greenhouse gasses. Such gases have tendency to trap heat from many sources like sun, electricity, burning coal, burning fuels, etc. Also prevent them to escape back to the atmosphere. Thus, collective effect of all green house gases increases temperature on earth and gives rise to many dangerous problems. Earlier, the earth was so cool and every natural cycles were going timely. However nowadays lots of dramatic fluctuations in the weather, climate, temperature, health, etc takes place. A vast increase in the temperature, sea level, and weather patterns has seen clearly in few last decades.

All changes because of the ever increasing global warming not coincidence however it caused by human activities and technological advancement. A little fluctuation in the level atmospheric carbon dioxide may cause high level significant effect on the global temperature. Increasing need of fossil fuels by people of industrial civilization is giving opportunity to rise in carbon dioxide emissions.

Effects Essay 6 (400 words)

Deforestation is also causing increase in the carbon dioxide through decreasing number of plants and exposing soil to the sunlight. Trees are best source to absorb CO2 from atmosphere. However what happen if we finish main source of reducing CO2 level. Our mal practices just to live luxurious life on daily basis are hugely altering the environment. It also endangering the lives of people in return. A little rise in the level of green house gases may hugely affect the global warming.

The use of a chemical called chlorofluorocarbon (CFC) in refrigerators, aerosol spray propellants are continuously damaging ozone layer. Thus increasing chance of heat to remain on earth by preventing them to escape back out to the environmental surface. Destruction of ozone layer increases incidence of many health disorders and diseases such as skin cancer, low immunity problems, etc. Global warming is damaging seasonal crops and marine food channel web, etc. It has affected the weather patterns and climate because of the increasing temperature and heat of the earth atmosphere.

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