生物质能和二氧化碳排放量外文文献

Linking biomass energy and CO2emissions in China using dynamic Autoregressive Distributed Lag simulations Danish a Recep Ulucakb aSchool of Economics and Trade Guangdong University of Foreign Studies 510006 Guangzhou China bErciyes University Faculty of Economics and Administrative Sciences Department of Economics Kayseri Turkey a r t i c l e i n f o Article history Received 28 August 2019 Received in revised form 25 November 2019 Accepted 30 November 2019 Available online 2 December 2019 Handling editor Zhifu Mi Keywords Biomass energy CO2emissions Real income Dynamic ARDL simulation China a b s t r a c t Can biomass energy mitigate CO2emissions in China Given increasing environmental pollution and global warming countries are switching to alternate energy sources that might help pollution reduction and mitigate climate change In this scenario biomass energy has received the attention of academic scholars and policy analyst alike What role biomass energy can play in environmental pollution remains uncertain so further investigation is necessary To this end this study explores the relationships of biomass energy and real income with CO2emissions for China Empirical evidence is based on the use of Jordan and Philips 2018 econometric tool dynamic Autoregressive Distributed Lag DARDL simula tions on data from 1982 to 2017 The results reveal a negative relationship between China s biomass energy consumption and CO2emissions suggesting that biomass energy consumption is helpful in reducing pollution Likewise biomass energy production reduces carbon emissions and might be the best alternative to fossil fuels Useful policy implications can be drawn related to biomass energy especially in attaining sustainable development goals 2019 Elsevier Ltd All rights reserved 1 Introduction Carbon dioxide CO2 in Earth s atmosphere has increased from 19 809 million tons to 33 431 million tons its highest level in recorded history in the recent decade BP 2018 posing challenges to humans and other life forms health Wang et al 2019 CO2 emissions contribute more than 60 percent of greenhouse gas GHG emissions IPPC 2014 and are accountable for global warming and climate change Sarkodie et al 2019 The rising environmental and health concerns are widely debated in the literature that addresses energy consumption that leads to CO2 emissions Danish et al 2017 particularly from fossil fuels as an energy source Danishetal 2017 Accordingly controlling climate change and environmental pollution has become an important issue Danish et al 2019 As a result of increasing environmental pollution demand for clean energy has increased The generation and use of clean energy sources like bioenergy and other renewables are the most effective tools for addressing rising environmental concerns Owusu and Asumadu Sarkodie 2016 Biomass energy use and development may be the foundation of a sustainable energy system by changing the pattern of energy pro duction and consumption which can effi ciently contribute to economic growth and strengthen environmental protections Mao et al 2018 Biomass energy an integral part of renewables have an important place in the debate on energy policy and strategies for sustainable development around the world Danish and Wang 2019 Many developing countries use biomass energy for nearly 35 percent of their energy needs which increases global con sumption to 13 percent Sarkodie et al 2019 Biomass energy is categorized into three groups Woody or solid biomass is generated in agro industrial plantations and forests urban trees bush trees and farm trees non woody biomass is produced in crop residues like straw leaves and plant stems processed residue like sawdust bagasse nutshells and husks and domestic waste like food rubbish and sewage animal waste is waste from animal husbandry Bildirici and Ozaksoy 2017 2018 The various kinds of biomass are used directly or indirectly for heat and electricity production transportation fuel and chemicals Humans have used biomass energy for thousands of years such as by burning wood for cooking or for warmth Biomass energy consumption includes direct Corresponding author School of Economics and Trade Guangdong university of foreign studies 510006 Guangzhou China E mail addresses khan danishkhan Danish r ulucak erciyes edu tr R Ulucak Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage https doi org 10 1016 j jclepro 2019 119533 0959 6526 2019 Elsevier Ltd All rights reserved Journal of Cleaner Production 250 2020 119533 consumption like combustion for heating cooking and industrial processes traditional consumption and indirect consumption by converting biomass into secondary energy Bildirici 2014 The world requires an overwhelming amount of energy to support future economic development Joselin Herbert and Unni Krishnan 2016 and bioenergy has the potential to address environmental problems like global warming climate change air pollution and acid rains by reducing CO2emissions and other pollutant gas emissions Bilgili et al 2017 China is the second largest energy consumer after the US Danish et al 2018 and has been one of the highest carbon emitters in the world since 2009 contributing nearly 28 percent of the world s total CO2emissions BP 2018 CO2emissions in China could peak by 2030 Wang et al 2019 Because it is the world s largest CO2emitter pressure on China to reduce CO2emissions is mounting Xu and Lin 2019 As a result China is playing an important role in achieving the goals of the Paris Agreement Biomass energy could help to resolve the environmental problem including environmental degradation and resource depletion that restricts the sustainable development of human society Xu and Chen 2018 In China biomass energy is amongst several rapidly developing energy source The installed capacity of biomass gen eration increased sharply from 1 4 Gigawatt GW in 2006 to 14 88 GW in 2017 The share of biomass energy is lowcompared to that of other renewables but China is planning toraise its share of biomass energy and its installation capacity to 15 percent and 30GW respectively by 2030 Fernandez 2019 Because of the considerable demand for biomass energy and the rapidly mounting CO2emissions levels a better understanding of the causal relationship between CO2emissions and biomass energy is necessaryfor China Tothis end thepresentstudyaims toexplore the linkages between biomass energy and CO2emissions consid ering real income foreign direct investment FDI and trade in a single multivariate framework for China from 1982 to 2017 This study makes several contributions It advocates for inte grated policy choices on sustainable development in China to develop a clear understanding of the links among the study s var iables The study can help the government of China in combating CO2emissions byunderstanding the role of biomass energy plays in mitigation The inquiry using the study s variables is fi rst of its kind for China In addition to biomass energy consumption the study considers biomass energy production to see whether it meets the sustainability criteria before designing policies for further inclusion of biomass energy into the energy mix Another contribution of the current investigation is the application of the novel time series data estimation approach developed by Jordan and Philips 2018 Deviating from the empirical time series data tools used in earlier studies such as the widely used auto distributive regressive model from Pesaran et al 2001 the current study uses the newly developed model the dynamic ARDL simulation method which accounts for the spurious effects of regressors on the dependent variable and addresses the multifaceted complications of the pre vailing ARDL model s behavior and interpretation Lastly this study adds tothe literature on environmental sustainability byevaluating the prevailing link between energy and environmental policies 2 Literature review The literature has highlighted the role of bioenergy develop ment in reducing GHG emissions increasing energy security eco nomicgrowthatthecostofbiodiversity extremewater consumption pollution rising energy prices and a possible decrease in food security Burg et al 2018 He et al 2018 Shao and Rao 2018 Bioenergy including waste and biomass consumption is a form of energy that takes climate change policy into account Bale zentis et al 2019 The literature has addressed both the negative and positive roles of biomass energy in environmental pollution For instance Katircioglu 2015 assessed the link be tween biomass energy and CO2emissions inTurkey using the ARDL method suggesting that biomass energy reduces pollution Bilgili 2012 found biomass energy helps in mitigating climate change in the US In another study Bilgili et al 2016 used the wavelet coherence approach to fi nd that biomass energy lowers CO2 emission in the US Shahbaz et al 2017 inferred similar results for the US using an autoregressive distributive lag ARDL bounding testing method Dogan et al 2017 explored the nexus between biomass energy and CO2emissions in biomass consuming coun tries fi nding positive role of biomass energy in reduction of carbon emissions Bale zentis et al 2019 argued that biomass energy re duces GHG more than other renewables do Shahbaz et al 2019 studied the signifi cance of biomass energy on the nexus between FDI and CO2emissions in the Middle East and North African countries using a generalized method of moment GMM proced ure and found a positive impact of biomass energy in pollution reduction Danish and Wang 2019 used the GMM method to investigate the role of biomass energy in reducing CO2emissions and found that biomass energy reduces environmental stress in Brazil Russia India China and South Africa BRICS countries Employing an innovative econometric approach dynamic ARDL Sarkodie et al 2019 considered the impact of biomass energy on GHG reduction using food and economic growth in a multivariate framework and found that biomass energy reduces carbon emis sions A few studies have not agreed with the positive effect of biomass energy in pollution reduction Mahmood et al 2019 Shahbaz et al 2018 Solarin et al 2018 and Ahmed et al 2016 argued that biomass energy has an insignifi cant impact on CO2 emissions At the same time some additional factors inCO2emissions such as international trade and FDI have been included in the literature for the purpose of avoiding specifi cation bias Ren et al 2014a concluded that both international trade and FDI in the Chinese industrial sector worsen environmental quality while Al Mulali et al 2015 found that international trade signifi cantly reduces CO2emissions in Europe Liobikien and Butkus 2018 found a similar result for a panel of 147 countries While Chang 2015 concludedthattradeliberalizationincreasesCO2emissions Zhang and Zhang 2018 found a negative impact of trade structure on CO2emissions in China Hille et al 2019 estimation result was that FDI reduces CO2emissions but in a study of several regions Shahbaz et al 2015 documented the adverse impact of FDI on CO2 emissions confi rming the pollution heaven hypothesis that the nature of FDI is that of a pollutant Behera and Dash 2017a confi rmed that FDI affects environmental quality by contributing to CO2emissions in the South and Southeast Asian SSEA region Liu et al 2018 concluded that an increase in FDI did not neces sarily degrade the environment and Waqih et al 2019 suggested that FDI does not play a role in raising environmental degradation in the SAARC region Sarkodie and Strezov 2019 considered the effect of FDI growth and energy on CO2emissions for developing countries using the Driscoll Kraay standard errors method and found that FDI helps achieve sustainable development goals These studies are among the few studies available concerning the nexus between biomass energy and CO2emissions and their fi ndings are ambiguous None of the studies mentioned have employed Jordan and Philips 2018 dynamic autoregressive distributed lag model and no study has been done in the Chinese context To fi ll these research gaps the current study highlights the role of biomass energy in CO2emissions for China considering the role of international trade and FDI in the environmental Kuznets curve EKC framework and using dynamic ARDL Danish R Ulucak Journal of Cleaner Production 250 2020 1195332 3 Materials and method 3 1 Data This study focuses on China and uses time series data for the period from 1982 to 2017 the longest available dataset 1CO2 emissions are collected in metric tons per capita and the data is gathered from the British Petroleum BP statistical review BP 2018 The CO2emissions data is available in millions of tons so we convert it to metric tons and then divide by the total population The real income is measured as gross domestic product GDP per capita in constant US dollars taking 2010 as the base year to represent the level of economic activity International trade is accumulating exports and imports as a percent of GDP and FDI is net infl ow as a percent of GDP The data for GDP per capita inter national trade and FDI are taken from the databank of World Bank World Bank 2018 In line with Danish and Wang 2019 and Mahmood et al 2019 the data for biomass energy consumption and production is measured per capita and gathered from the Global Material Flows Database 3 2 Model construction The study s empirical approach is borrowed from recent litera ture Danish and Wang 2019 Mahmood et al 2019 Shahbaz et al 2019 to examine the relationships of real income and biomass energy with CO2emissions taking trade ratio and FDI into account and can be express as in Equation 1 Ln CO2 t a0 b1 Yt b2 BMECt b3 BMEPt b4Ln TRt b5Ln FDIt mt 1 where t is time CO2is carbon dioxide emissions Y is real income per capita BMEC is biomass energy consumption BMPE is biomass energy production TR is the trade ratio FDI is foreign direct in vestment andmis the stochastic error term The study s empirical model includes the control variables in ternational trade and FDI Previousstudies have found that both FDI and trade either reduce or increase CO2emissions For example Ren et al 2014b argued that a mounting trade surplus signifi cantly contributes to CO2emissions but estimated the scale and composition effects to be positive and highly signifi cant The indi rect technique effect does much in controlling air pollution Hille et al 2019 and FDI has a detrimental effect on the environment Bakirtas and Cetin 2017 Solarin et al 2017 The spatial spillover effect of FDI is negatively correlated with environmental pollution as well Jiang et al 2018 and Danish et al 2018 argued that FDI is a pollutant in nature since outdated technology transferred in the form of FDI is polluting and produces air emissions Danish et al 2018 Yu and Xu 2019 found that FDI is helpful in reducing pollution and did not validate the pollution heaven hypothesis FDI s environmental impact worsens as the scale of its infl ows in creases Pazienza 2019 observed FDI s spillover effect as well citing that pollution decreases with technology innovation and that implementation of environmentally friendly regulation and cleaner production modes occurs Pazienza 2019 Waqih et al 2019 confi rmed the absence of the pollution heaven hypothesis and concluded that FDI reduces pollution Wang and Liu 2019 Thus FDI s expected sign is either positive or negative Trade another factor in the study s model has been widely debated in the literature as a potential factor in CO2emissions Several studies have found that international trade reduces envi ronmental pollution Al Mulali et al 2015 Managi et al 2009 Wang et al 2018 Zhang and Zhang 2018 and that cleaner tech nology transfer through trade improves energy effi ciency and re duces CO2emissions in turn Danish et al 2017 However Hakimi and Hamdi 2016 found that free trade harms environmental quality When trade barriers become relaxed weak environmental standards can result because of increased trade activities that involve the transfer of dirty technologies Danish and Wang 2019 The impact of both FDI and international trade on CO2emissions then is a matter of debate so the expected sign may be negative or positive 3 3 Methods In line with recent research work Khan et al 2019 Sarkodie et al 2019 this study uses the dynamic ARDL model based on dynamic simulations which can be expressed as D y t a0 q0 y t 1 q1 x1 t 1 qk xk t 1 X p i 1 aiD y t 1 X q1 j 0 b1jD x1 t j X qk j 0 bkjD xk t j mt 2 where y changes in the dependent variable a0 is intercepted andt 1 showstheindependent variables maximum level of p and with lags qk in the fi rst differences D operator with the error term intimet Thenullhypothesisofnointegration H0 q0 q1 qk 0 against the alternate hypothesis H0 q0 q1 qk 0 is tested An F value greater than the upper bound critical value I 1 supported by approximate p values results in rejecting the null hypothesis of no co integration Kripfganz and Schneider 2018 pathways for critical values are used while examining Pesaran et al 2001 ARDL bound testing procedure Jordan and Philips 2018 established an innovative time series data model the dynamic ARDL model which was proposed towork out and eradicate the complications in previous ARDL methods used for investigation of the short run and the long run relation ship among variables This novel method can estimate stimulate and plot to predict automatically spurious changes in the depen dent variable that are due to a regressor while other factors remain constant Jordan and Philips 2018 Sarkodie et al 2019 For the dynamic stimulated ARDL technique to be applicable the data se ries should be of order one and cointegration should exist among the variables under consideration The current study s data series fulfi lls these requirements The dynamic ARDL methods use up to 5000 simulations of the vector of parameters using multivariate normal distribution Sarkodie et