Applying environmental Kuznets curve framework to assess the nexus of industry, globalization, and CO2 emission

Aslam, Bilal; Hu, Jinsong; Hafeez, Muhammad; Ma, Deqing; AlGarni, Tahani Saad; Saeed, Muhammad; Abdullah, Muhammad; Hussain, Shahid

doi:10.1016/j.eti.2021.101377

Cited by 70 publications

(34 citation statements)

References 59 publications

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“…The impact of carbon emissions on green credit is similar to the overall national sample in the East and Middle of China but less in the West. Previous studies have also found a two-way causal relationship between financial development and carbon emissions, which is consistent with the conclusions of our empirical research [60,61]. Lei et al [62] have shown that the positive impact brought by broad money can promote the use of renewable energy and reduce the consumption of non-renewable energy so as to reduce carbon dioxide emissions.…”

Section: Impulse Response Analysissupporting

confidence: 92%

Is Green Credit a Good Tool to Achieve “Double Carbon” Goal? Based on Coupling Coordination Model and PVAR Model

Zheng

2021

Sustainability

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China attaches importance to the combination of credit policy and environmental protection, tries to use credit policy tools to protect ecology and pollution prevention, and prevent environmental credit risk. With the proposal of the goal of “carbon peak and carbon neutralisation” (“double carbon”), green credit is also regarded as an important policy tool to achieve this goal. Firstly, this paper selects the time series data of green credit at the national level from 2013 to 2019 according to the official statistics and evaluates the coupling and coordinated development of credit system and environmental system based on the coupling coordination model. The results show that the two systems show well-coordinated development, but the interaction has annual fluctuations. Secondly, by calculating the provincial carbon emissions and green credit panel data from 2005 to 2019, the dynamic internal mechanism is analysed based on Panel Vector Autoregression (PVAR) model. It is found that green credit has a significant inhibitory effect on carbon emissions. The research results of this paper provide an overall evaluation of the quantity and quality of green credit for China’s banking industry. It also provides reasonable and effective support for green credit as a policy tool to promote realising the “double carbon” goal. In addition, China should maintain the consistency, stability and durability of green credit policy and continue to contribute to the low-carbon transformation of the economy and society.

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Section: Impulse Response Analysissupporting

confidence: 92%

Is Green Credit a Good Tool to Achieve “Double Carbon” Goal? Based on Coupling Coordination Model and PVAR Model

Zheng

2021

Sustainability

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“…Previous studies suggest that not only economic growth but also other factors, such as economic complexity (Kirikkaleli & Adebayo, 2021a;Rjoub and Adebayo, 2021;Ahmad et al, 2021c;Doğan et al, 2021), renewable energy (Fareed et al, 2021;Chien et al, 2021;Kirikkaleli and Adebayo, 2021b;Soylu et al, 2021) globalization (Aslam et al, 2021;Anser et al, 2021;Chien et al, 2021) and others (An et al, 2019;Miao et al, 2019;Zhang et al, 2019;Mohammad et al, 2020;Alola et al, 2021;Ahmed et al, 2021c;Ramzan et al, 2021;Shan et al, 2021;Chen et al, 2019) can impact environmental deterioration. Meanwhile, the main driver of CO 2 emissions is economic growth or per capita income, since all other variables are directly and indirectly related to CO 2 emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The outcome revealed that the effect of economic growth on CO 2 emissions is positive, whereas, the influence of renewable energy on CO 2 emissions is revealed to be insignificant. The research of Aslam et al (2021) uncovered the effect of economic growth and globalization on CO 2 emissions for Malaysia within the period from 1971 to 2016. The outcomes of this study revealed that there is a direct interaction between economic growth and CO 2 emissions, however, a similar connection was evident between globalization and CO 2 emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Does Globalization Moderate the Effect of Economic Complexity on CO2 Emissions? Evidence From the Top 10 Energy Transition Economies

Ramzan

Awosusi

et al. 2021

Front. Environ. Sci.

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The association between economic complexity (sophisticated economic structure) and carbon emissions has major implications for environmental sustainability. In addition, globalization can be an important tool for attaining environmental sustainability and it may also moderate the association between economic complexity and carbon emissions. Thus, this research examines the effects of economic complexity, economic growth, renewable energy, and globalization on CO2 emissions in the top 10 energy transition economies where renewable energy and globalization have greatly increased over the last 3 decades. Furthermore, this study also evaluates the joint effect of globalization and economic complexity on carbon emissions. Keeping in view the presence of slope heterogeneity and cross-sectional dependence in the data, this research utilized second-generation unit root tests (CIPS and CADF), Westerlund cointegration approach, and CS-ARDL and CCEMG long-run estimators over the period of 1990–2018. The results affirmed the presence of cointegration among the considered variable. Long-run findings revealed that globalization, renewable energy, and economic complexity decrease carbon emissions. Conversely, economic growth increases carbon emissions. Moreover, the joint impact of economic complexity and globalization stimulates environmental sustainability. Based on these findings, the government of these groups of economies should continue to expand the usage of renewable energy. They should also promote interaction with the rest of the world by adopting the policy of opening up.

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“…The BDS test also reported the non-linearity in CO 2 and labour productivity variables and thus we can apply the NARDL. ARDL is most useful for the small data set variables (Aslam et al 2021 ; Ahmed et al 2019 ) (Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

Management of human resources in the green economy: Does green labour productivity matter in low-carbon development in China

Soomro

Wang

Tunio

et al. 2021

Environ Sci Pollut Res

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Environmentally friendly economic development has become a global concern, whereas the existing literature has ignored the human resources management in the green economy. This study utilizes the basic Cobb–Douglas production function and examines the nonlinear effect of labour productivity on the environment in China. Non-linear findings infer that a positive change in labour productivity has a positive and negative change in labour productivity, and has a negative effect on CO 2 emissions in the short run, while results persisted and stable in the long run in China. The crux of this study is that labour productivity is vital for understanding the evolution of a green economy. Conventionally, capital productivity and energy consumption also tend to follow dirty productivity growth and thus, increased environmental pollution. Indeed, research and development is a forceful input to environmental quality. Based on findings, policymakers should need to focus on human resource productivity, green business, and ecosystem protection.

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Applying environmental Kuznets curve framework to assess the nexus of industry, globalization, and CO2 emission

Cited by 70 publications

References 59 publications

Is Green Credit a Good Tool to Achieve “Double Carbon” Goal? Based on Coupling Coordination Model and PVAR Model

Is Green Credit a Good Tool to Achieve “Double Carbon” Goal? Based on Coupling Coordination Model and PVAR Model

Does Globalization Moderate the Effect of Economic Complexity on CO2 Emissions? Evidence From the Top 10 Energy Transition Economies

Management of human resources in the green economy: Does green labour productivity matter in low-carbon development in China

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