Search results

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

To cope with the severe situation of the global climate, China proposed the “30 60” dual-carbon strategic goal. Based on this background, the purpose of this paper is to investigate scientifically and reasonably the interprovincial pattern of China’s power carbon emission intensity and further explore the causes of differences on this basis.

Considering the principle of “shared but differentiated responsibilities,” this study measures the carbon emissions within the power industry from 1997 to 2019 scientifically, via the panel data of 30 provinces in China. The power carbon emission intensity is chosen as the indicator. Using the Dagum Gini coefficient to explore regional differences and their causes.

The results of this paper show that, first, China’s carbon emission intensity from the power industry overall is significantly different. From the perspective of geospatial distribution, the three regions have unbalanced characteristics. Second, according to the decomposition results of the Gini coefficient, the overall difference in power carbon emission intensity is generally expanding. The geospatial and economic development levels are examined separately. The gaps between the eastern and economically developed regions are the smallest, and the regional differences are the source of the overall disparity.

Further exploring the causes of differences on this basis is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies. This study provides direction for analyzing the green and low carbon development of China’s power industry.

As an economic indicator of green and low-carbon development, CO₂ intensity of power industry can directly reflect the dependence of economic growth on the high emission of electricity and energy. and further exploring the causes of differences on this basis is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies.

For a long time, with the rapid economic development, resulting in the unresolved contradiction between low energy efficiency and high carbon emissions. To this end, scientifically and reasonably investigating the interprovincial pattern of China’s power carbon emission intensity, and further exploring the causes of differences on this basis, is crucial for relevant departments to formulate differentiated energy conservation and emission reduction policies.

Third, considering the influence of spatial factors on the convergence of power carbon emission intensity, a variety of different spatial weight matrices are selected. Based on the β-convergence theory from both absolute and conditional perspectives, we dig deeper into the spatial convergence of electricity carbon emission intensity across the country and the three regions.

The purpose of this paper is to examine the effects of energy technological innovation on carbon emissions in China from 2001 to 2016.

Conditional mean (CM) methods are first applied to implement our investigation. Then, considering the tremendous heterogeneity in China, quantile regression is further employed to comprehensively investigate the potential heterogeneous effect between energy technological innovation and carbon emission intensity.

The results suggest that renewable energy technological innovation has a significantly positive effect on carbon emission intensity in lower quantile areas and a negative effect in higher quantile areas. Contrarily, fossil energy technological innovation exerts a negative correlation with carbon emission intensity in lower quantile areas and a positive effect on carbon emission intensity in higher quantiles areas.

Considering that energy consumption is the main source of CO₂ emissions, it is of great importance to study the impact of energy technological innovation on carbon emissions. However, the previous studies mainly focus on the impact of integrated technological innovation on carbon emissions, ignoring the impact of energy technological innovation on carbon emissions mitigation. To fill this gap, we construct an extended STIRPAT model to examine the effects of renewable energy technological innovation and fossil energy technological innovation on carbon emissions in this paper. The results can provide a reference for the government to formulate carbon mitigation policies.

Reductions in emissions intensity have been expressed in commitments of many countries’ intended nationally determined contribution. Energy structure adjustment is one of the main approaches to reduce carbon emissions. This paper aims to study the causal relationship between carbon emission intensity and energy consumption structure in China based on path analysis.

After data collection, this paper performs correlation analysis, regression and path analysis.

Correlation results display clear collinearity among energy structure variables. Regression finds that coal, oil, natural gas and technology can be used as indicators for carbon intensity while primary electricity has been excluded. Path analysis shows that coal had the largest direct and positive impact on emission intensity. Natural gas had a positive direct and negative indirect effect through its negative relationship with coal on emission intensity. Technology has the largest negative elasticity while all fossil energies are positive. Results indicate a negative effect of energy structure adjustment on China’s national carbon intensity.

Given the major role of China in global climate change mitigation, significant future reductions in China’s CO₂ emissions will require transformation toward low-carbon energy systems. Considering the important role in mitigating global climate change, China needs to transition toward a low-carbon energy system to significantly reduce its carbon intensity in the future.

Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance.

This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix.

The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications.

This paper provides an insight into the current state and the future changes in carbon emissions.

The aim of this study was to investigate the impact of low-carbon city pilots (LCCPs) policy using Chinese city-level data from 2009 to 2018 and examine the mechanisms of LCCP policy using a mediation effect model.

The authors measured carbon emissions by high-resolution carbon emission data and used difference-in-difference (DID) and propensity matching score-difference-in-difference (PSM-DID) model to investigate the relationship between LCCP policy and urban carbon intensity. The complex relationship between policy and carbon intensity was evaluated through a mediation model.

The results show that LCCP policy can reduce urban carbon intensity (−0.287), but its effects are different in different sectors. The impact of LCCP policy is greater in the industrial enterprise sector than in the transport sector than in the agricultural sector. Second, the authors find that LCCP policy under market-driven is more effective than government intervention. Third, there is a spillover effect of LCCP policy, which is decreasing with distance. Finally, the authors explore the mechanisms of LCCP policy from multiple perspectives, such as optimizing industrial structure, green areas, promoting public transport travel, population migration and innovation. In addition, the flow of these factors can also explain the spillover effects of LCCP policy.

This study confirms that LCCP policy is an effective tool for achieving urban sustainable development. Government policy-makers should consider the differences in the impacts of LCCP policy in different sectors and the spillover effects of LCCP policy. And, it shows that the effects of LCCP policy are larger by market-driven. These findings imply that the government should take full account of city characteristics and marketisation processes when formulating carbon reduction policies.

This study analyzed the relationship between LCCP policy and urban carbon intensity based on high-resolution carbon emission data. Urban panel data are used to discuss the impacts of LCCP policy under government intervention and market-driven and the mechanisms at play. The study reveals that LCCP policy mainly acts on the industrial enterprise sector, the spillover effects and the market-driven effects.

This paper aims to identify the significance of carbon emissions reporting for investment banking.

Functionaries at selected financial institutions in the USA, Europe and Australia are interviewed. Carbon emissions reporting methods used by companies are identified using desk research. A proposal from a non‐state actor called the Climate Disclosure Standards Board for general‐purpose carbon emissions reporting is assessed using participant observation. The data gathered are interpreted through a semiotic lens, with focus on the placement, content, and style of reporting, and combining with a functional perspective of decision‐usefulness.

Environmental investing for well‐diversified investors constitutes a discourse of the imaginary. Financialised constructs have been used to represent heavier polluters as superior “carbon performers” (the imaginary), while reported variations in industrial carbon emissions levels have been ignored in asset allocation decisions (the actual). Environmental investing is conditioned by four factors: exclusion of carbon emissions in constructions of firm value; diverse methods used by firms to calculate, measure and report carbon emissions; the appropriate venue for such reporting; and the quantum of data contained therein. Carbon emissions reports have had some use in investors' assessments of firms' corporate governance.

Risk assessment is likely to be erroneous if using measures that deflate carbon emissions by firms' revenues. This may not matter much as carbon reporting in the hands of investors appears linked to imaginary signification more so than actual portfolio decisions.

The paper contributes to work on the participation of institutional investors in environmental investing and establishes a foundation for future research in general‐purpose reporting on greenhouse gas emissions. Supplemented by desk research, the study uses interviews to provide insights into investors' motivations for environmental investing, and how they use company‐issued carbon reports.

This paper aims to clarify the relationship between foreign direct investment (FDI) and carbon intensity. This study uses the dynamic panel data model to study and provide fresh evidence for the issue.

This study first uses the dynamic panel data model to consider the endogeneity problem, and applies a system-generalized method of moments estimator to study the effect of FDI on carbon intensity using the panel data of 188 countries during 1990-2013.

The result shows that FDI has a significant negative impact on carbon intensity of the host country. After considering the other factors, including share of fossil fuels, industrial intensity, urbanization level and trade openness, the impact of FDI on carbon intensity is still significantly positive. In addition, FDI also has a significant negative impact on carbon intensity of high-income countries and middle- and low-income countries.

This paper offers two contributions to the literature on the effect of FDI on carbon intensity. From a methodological perspective, this paper is the first to apply a dynamic panel data model to study the effect of FDI on carbon intensity using worldwide panel data. Second, this paper is the first to analyze the effect of FDI on carbon intensity in different countries with different income levels separately.

This study aims to determine the influence of corporate governance characteristics on carbon emission disclosure in an emerging economy.

The study is based on S&P BSE 500 Indian firms for the period of 6 years from 2016–2017 to 2021–2022. The panel data regression models are used to gauge the association between corporate governance and carbon emission disclosure.

The empirical findings of the study support the positive and significant association between board activity intensity, environment committee and carbon emission disclosure. This evinced that the board activity intensity and presence of the environment committee have a critical role in carbon emission disclosure. On the contrary, findings reveal a significant and negative relationship between board size and carbon emission disclosure.

The present study provides treasured insights to regulators, policymakers, investors and corporate managers, as the study corroborates that various corporate governance characteristics exert significant influence on carbon emission disclosure.

The current research work provides novel insights into corporate governance and climate change literature that good corporate governance significantly boosts the carbon emission disclosure of firms. Previous studies examining the impact of corporate governance on carbon emission disclosure ignored emerging economies. Thus, the current work explores the role of governance mechanisms on carbon emission disclosure in an emerging context. Further, to the best of the author’s knowledge, the current study is the first of its kind to investigate the role of corporate governance on carbon emission disclosure in the Indian context.

This study is aimed at better understanding the evolution of inequality in carbon emission in intraincome and interincome groups in the world, and then to uncover the driving factors that affect inequality in carbon emission.

The approach is developed by combining the Theil index and the decomposition technique. Specifically, the Theil index is used to measure the inequality in carbon emissions from the perspective of global and each income group level. The extended logarithmic mean Divisia index was developed to explore the driving factors.

This study finds that the inequality in carbon emissions of intraincome group is getting better, whereas the inequality in carbon emission of interincome group is getting worse. And the difference in global carbon emissions between income groups is the main source of global carbon emission inequality, which is greater than that within each income group. In addition, the high-income group has transferred their carbon emissions to upper-middle income group by importing high-carbon-intensive products to meet the domestic demand, while lower-middle-income group do not fully participate in the international trade.

To alleviate the global carbon inequality, more attention should be paid to the inequality in carbon emission of interincome group, especially the trade between high-income group and upper-middle income group. From the perspective of driving factors, the impact of import and export trade dependence on the per capita carbon emissions of different income groups can almost offset each other, so the trade surplus effect should be the focus of each group.

In order to consider the impact of international trade, this study conducts a comprehensive analysis of global carbon emissions inequality from the perspective of income levels and introduces the import and export dependence effect and the trade surplus effect into the analysis framework of global carbon emission inequality drivers, which has not been any research carried out so far. The results of this paper not only provide policy recommendations for mitigating global carbon emissions but also provide a new research perspective for subsequent inequality research.