Reduce Carbon Emission Intensity Research Articles

Political Sensitivity and Carbon Emissions: Evidence From a Quasi-Natural Experiment of Leadership Change in China

While there is extensive literature assessing the impact of command-and-control and market-incentive environmental regulation on carbon emissions, it overlooks the fact that command-and-control environmental regulation and market-incentive environmental regulation are the embodiment of the national concept of green development. When a national leader with a green development philosophy is in power, local governments (due to the competition among them) adjust the economic development strategies and targets in their jurisdictions according to the leaders’ philosophy, resulting in differences in urban carbon emissions. Based on Chinese urban panel data from 2010 to 2016, this study uses a difference-in-differences approach to assess the impact of a national leader with a green development philosophy in power on carbon emissions in cities with different political sensitivities and the mechanisms of the impact. Our study finds that after the change in national leadership, the carbon intensity of high politically sensitive cities decreases significantly compared to low politically sensitive cities. We further find that increasing urban green areas and limiting the development of high-polluting industries are two ways to reduce carbon emission intensity in highly politically sensitive cities. A heterogeneity test demonstrates that the higher the level of economic development of the region where the city is located, the fewer the number of industrial enterprises in the city, the lower the total industrial output value of the city, and the more domestic enterprises in the city, proving that the change of national leaders more significantly impacts the city’s carbon emission. Our study suggests that policymakers should pay careful attention to the system of appraisal of officials, urban greening and development of highly polluting industries, and differentiated environmental policies.

Does the new energy demonstration cities construction reduce CO2 emission? Evidence from a quasi-natural experiment in China.

Based on the panel data of 279 cities in China from 2003 to 2017, this paper regards the new energy demonstration cities (NEDC) construction as a quasi-natural experiment, using the double-fixed effect model and the difference-in-differences (DID) method to test its local carbon emission reduction effect and transmission mechanisms and further explores the impact of NEDC on neighboring carbon emissions. Results show that (1) NEDC reduces carbon emission intensity and per capita carbon emission significantly and shows dynamic sustainability. The policy effect has shown a trend of increasing year by year. Moreover, there is significant heterogeneity in the carbon emission reduction effect of NEDC, which produces more significant policy effect in large-scale and non-resource-based cities. (2) NEDC construction reduces carbon emission through green technology innovation effect, innovative elements agglomeration effect, and total factor productivity improvement effect. In terms of the contribution of reducing carbon emission intensity and per capita carbon emission, total factor productivity accounts for 36.7% and 21.5%, respectively, green technology innovation accounts for 18.6% and 23.9%, respectively, the contribution of R&D personnel agglomeration is 7.5% and 8.3%, respectively, and the contribution of R&D capital agglomeration is 5.9% and 9.5%, respectively. (3) From the perspective of spatial effect, the impact of NEDC on carbon emissions presents a "siphon" effect; that is, although NEDC reduces local carbon emissions, it has produced the phenomenon of transfer to neighboring areas, accelerating the increase in the carbon emission intensity of neighboring areas.

A two-phase factorial input-output model for analyzing CO2-emission reduction pathway and strategy from multiple perspectives – A case study of Fujian province

In this study, a two-phase factorial input-output model (TFIOM) is developed for carbon dioxide (CO2) emission mitigation, through integrating input-output model (IOM) and two-phase factorial analysis (TFA) into a general framework. TFIOM can identify the main sectors of CO2 emission, quantify the individual and interactive effects of the main factors (i.e. various CO2 management strategies implemented on different sectors) on reduction target, as well as seek CO2-emission reduction pathways and strategies from multiple perspectives (i.e. energy structure adjustment strategy (ESAS), technology upgrade strategy (TUS), and industrial structure adjustment strategy (ISAS)). The developed TFIOM is applied to Fujian province (in China), and results reveal that industries of chemical, non-metallic, metal smelting (MSI), and electric heating (EGW) are the main CO2-emission sectors (i.e. occupying 57.6% of direct emission and 56.4% of indirect emission). The joint application of ESAS (applied to EGW) and TUS (applied to MSI) has the best effect in reducing carbon emission intensity (decreasing by 24.5%). Among all scenarios, Scenario 115 shows the most desirable emission-reduction effect (i.e. carbon emission intensity, energy intensity, total direct emission, and total indirect emission would respectively decrease by 22.3%, 7.6%, 21.3% and 5.1%, and the cost of reducing CO2 per ton would be 182 RMB¥).

Decomposition and prediction of China's carbon emission intensity towards carbon neutrality: From perspectives of national, regional and sectoral level

China has actively participated in global climate governance and put forward its ambitious carbon neutrality target. The formulation of scientific plans has become the primary concern of the policy makers, especially for the 14th and 15th Five-Year Plans (FYP) which are important periods to secure the neutrality pledge and transform the whole economy. Since the carbon emission intensity play a key role in achieving carbon neutrality, it is necessary to summarize and explore the evolution trend of carbon emission intensity as well as its driving factors. Therefore, an integrated decomposition framework is developed to study the carbon emission intensity in the past three FYPs from the national, regional and industrial levels. Furthermore, towards the carbon neutrality target, moderate scenario and advanced scenario are designed to predict the future evolution trend of the carbon emission intensity and driving factors in the 14th and 15th FYPs (2021–2030). The main results are as follows: (1) During the three FYPs, factor substitution is the main force contributing to the decreased carbon emission intensity, but this effect gradually decreased. This indicates that it is an inevitable trend to further promote internal optimization and reform of energy system. (2) The change of energy structure exerts a positive effect on the carbon emission intensity decline, but it is not significant, especially in the industrial sector. (3) With the rich factor endowment, central and eastern regions can reduce carbon emission intensity through factor substitution and industrial structure transformation, while the western region is not. (4) In the future, the role of industrial structure optimization and technology progress will be gradually significant. Finally, our findings provide practical guidance on achieving carbon emission intensity reduction and enlightenments on policymaking.

The Impact of Digital Finance Development on CO<sub>2</sub> Emissions in China

The problem of environmental pollution caused by massive CO2 emissions is becoming increasingly serious. As an environment-friendly financial service, digital finance is conducive to the development of a low-carbon economy. Based on the panel data of 287 cities in China from 2011 to 2019, this paper uses a two-way fixed-effect model and an intermediary model to study the impact of digital finance on carbon emissions. The empirical analysis results show that: in the whole of China, digital finance has an obvious effect on reducing carbon dioxide emission intensity; the impact of digital finance on carbon emission intensity has regional heterogeneity, and the development of digital finance in eastern China can play a significant role in reducing carbon dioxide emissions. The impact of digital finance on carbon emission intensity is dimensionally heterogeneous, and the development of the usage depth of digital finance and the digitization level can reduce carbon emission intensity; technological progress plays a partial intermediary role in the process of digital finance affecting carbon emissions. Digital finance can drive low-carbon development by promoting the optimization and upgrading of technology. Based on the above conclusions, this paper puts forward corresponding suggestions for the development of China’s green and low-carbon economy.

Quality Evaluation of Environmental Accounting Information Disclosure of Y Nonferrous Metal Company Based on AHP‐FCE Model

Environmental accounting information disclosure is an important way to achieve the goal of “carbon peak carbon neutrality”, which plays a role in the link between economic development and environmental protection. Select Y Non‐Ferrous Metal Company for case analysis, use the AHP‐FCE model for comprehensive evaluation of environmental accounting information quality of the company, which use analytic hierarchy process to determine the evaluation factors and factors of weight value, and then use fuzzy synthesis method for multi‐level fuzzy comprehensive evaluation. The results show that as a state‐owned enterprise, the quality level of Y company’s environmental accounting information disclosure is in the “general” level in the past 5 years, and the intensity of government environmental regulation will promote the quality of the company’s environmental accounting information disclosure. Non‐ferrous metal companies should enhance the concept of environmental protection, strengthen the awareness of environmental accounting information disclosure, promote technological innovation in energy conservation and emission reduction, and reduce carbon emission intensity. The government should also provide a favorable social environment and policy support for the non‐ferrous metal industry to implement environmental accounting information disclosure, formulate relevant laws and regulations, and continuously strengthen supervision.

Optimal economic restructuring to reduce carbon emissions intensity using the projected gradient algorithm

Optimal economic restructuring to reduce carbon emissions intensity using the projected gradient algorithm

Will researching digital technology really empower green development?

The information industry has become a “new engine” driving the growth of the world economy. However, there are many controversies about whether digital technology can reduce the intensity of carbon emissions. Based on OECD data, KPWW method and multiple panel regression, this paper explores the impact and mechanism of digital technology innovation and technology spillover to the domestic carbon emission intensity. Through impulse response analysis and variance decomposition, the comprehensive impact of digital technology on carbon intensity is clarified. This paper concludes that technology innovation in the information industry will increase the intensity of carbon emissions, while cross-industry technology spillovers are persistent for reducing the intensity of domestic carbon emissions. Since the emission reduction effect of technology spillover is greater than the emission increase effect of technology innovation, the digital technology would empower domestic green development. Increasing the proportion of non-fossil energy use and optimizing the industrial structure are effective mechanisms for digital technology innovation to reduce carbon emission intensity.

Does the Belt and Road Initiative Increase the Carbon Emission Intensity of Participating Countries?

AbstractThe impact that the Belt and Road Initiative (BRI) has had on carbon emissions is a hotly debated issue. Using a panel dataset of 178 countries from 2002 to 2017, and applying the quantile difference‐in‐difference method in different industries, this study finds that, first, the BRI overall tends to reduce the carbon emission intensity of BRI countries. Second, the impact of BRI on reducing the carbon emission intensity is significant for BRI countries at higher (0.8 and 0.9) and lower (0.2 and 0.3) carbon emission intensity quantiles but it is insignificant for those at medium levels. Third, the BRI has significant impacts on reducing carbon emission intensity in the energy‐intensive industries, and this effect is the highest at the quantile level of 0.9 for all three industries considered here: transportation, electric and heating, and manufacturing and construction. These results indicate that establishing BRI cooperation with China will improve the environment and enhance the sustainable development ability of BRI countries.

Adjusting Industrial Structure and Reducing Carbon Emission Intensity: International Comparison and Experience Enlightenment

Adjusting Industrial Structure and Reducing Carbon Emission Intensity: International Comparison and Experience Enlightenment

Spatial effects of carbon emission intensity and regional development in China.

Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China's provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China's 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China's provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China's carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity.

A factorial CGE model for analyzing the impacts of stepped carbon tax on Chinese economy and carbon emission

Carbon tax is a powerful incentive to mitigate carbon emissions and promote energy revolutions. It is of vital importance to systematically explore and examine the socio-economic impacts of levying a carbon tax, such that desired compromises among socio-economic and environmental objectives can be identified. In order to fill the research gap on the stepped carbon tax, this study is to develop a factorial computable general equilibrium (FCGE) model for examining the interactive effects of multiple policy options (e.g., grouping of emission intensity/level, and relevant tax rates), and supporting the formulation of desired carbon-mitigation policies. It is discovered that (1) carbon tax of 18.37 to 38.25 Yuan/ton is a reasonable policy alternative for China; (2) the stepped carbon tax (high level on coal-related fuels) is more efficiency than conventional carbon tax policy; (3) the positive effects for reducing carbon emission intensity can be strengthened with an increasing step range; (4) interactive effects between stepped carbon taxes on coal-related energies and crude oil related energies should be jointly considered by the policy makers.

How does ecological civilization construction affect carbon emission intensity? Evidence from Chinese provinces’ panel data

Ecological civilization construction is a new concept and trend in the era of China's high-quality development. It requires the collaborative propulsion of an ecological economic civilization, ecological social civilization, and ecological environment civilization. Reducing carbon emission intensity is an important issue facing the Chinese government in the backdrop of global warming. Thus, studying the influence of ecological civilization construction on carbon emission intensity from different perspectives has important theoretical and practical significance. In this study, the influences of the three subsystems of an ecological civilization on carbon emission intensity are empirically analyzed using Chinese provincial panel data from 2004 to 2016 and a spatial Durbin model based on the STIRPAT model. First, the Moran's I of carbon emission intensity in Chinese provinces was between 0.425 and 0.473. This indicates positive spatial correlation and illustrates that the carbon emission intensity of China's provinces can influence each other. The reasons behind this correlation include close ties between neighboring provinces and similarities in natural, economic, and social characteristics. Second, the correlation coefficients of ecological economic civilization, ecological social civilization, and ecological environment civilization to carbon emission intensity are −4.743139, 2.865884, and −0.3246447, respectively. This illustrates that an ecological economic civilization and ecological environment civilization can reduce carbon emission intensity, while an ecological social civilization can increase it. To reduce total carbon emission intensity, the three subsystems of ecological civilization should have a negative relationship with carbon emission intensity, so the effect of ecological social civilization on carbon emission intensity should be changed. Third, the spatial spillover effect of ecological social civilization did not pass the significance test. The correlation coefficients of spatial spillover effect to ecological economic civilization and ecological environment civilization are 2.046531 and −3.238323, respectively. Improving the ecological economic civilization can increase the carbon emission intensity of periphery provinces, while improving the ecological environment civilization can reduce it. Thus, it is necessary to enhance cooperation between periphery provinces and establish a trans-provincial cooperation mechanism for reducing carbon emissions.

Does financial development mitigate carbon emissions? Evidence from heterogeneous financial economies

In this paper, we investigate the impact of financial market development on carbon emission intensity, taking into account the various stages of financial development among countries. Utilising the instrumental variable generalised method of moment approach and a comprehensive panel dataset of a total of 83 countries over the period 1980–2015, we show that the overall financial market development and its sub-measures such as financial market depth and efficiency reduce carbon emission intensity in the developed and emerging financial economies. However, an opposing effect is found in the frontier financial economies. For standalone financial economies, the results show that the overall financial market development and its sub-indicators have no direct impact on carbon emission intensity. Finally, the non-linear and the moderating effects of financial market development on carbon emission intensity differ across countries at different stages of financial development. The policy implications are also discussed. • Comparative analysis of financial market development on CO 2 emission intensity • Financial markets development reduces CO 2 emission intensity in developed and emerging financial economies. • Financial markets development increases CO 2 emission intensity in frontier financial economies. • Financial markets development has no linear effect on CO 2 emission intensity in standalone financial economies. • Financial markets development moderates economic growth and energy to influence CO2 emission intensity.

The heterogeneous effect of driving factors on carbon emission intensity in the Chinese transport sector: Evidence from dynamic panel quantile regression

The transport sector is becoming a key sector for China to accomplish its targets for reducing carbon emission intensity (CEI). Identifying the dominant factors driving CEI of the transport sector is important for CEI mitigation. This paper applied dynamic panel quantile regression to explore the effect of driving factors on CEI in the Chinese transport sector at the provincial level during 2000–2016. The empirical findings indicate that economic growth has a positive influence on CEI at low quantiles, whereas this effect is the opposite at high quantiles. Further, the findings show an inverted U-shaped pattern between economic growth and CEI at low quantiles, which validates the Environmental Kuznets Curve hypothesis in low-CEI provinces. Energy intensity positively influences CEI, with the greatest impact occurring at higher quantiles. Among the lowest CEI provinces, private vehicles and cargo turnover appear to contribute to CEI, and a positive impact of urbanization exists, except at the 5th and 30th quantiles. In conclusion, policy implications for effectively promoting the CEI abatement in the transport sector are discussed.

Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network

Abstract To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040. Random forests (RF) method is used to select the important carbon emissions influencing factors to reduce data redundancy. The superiority of IFWA-GRNN forecasting model is verified by historical data from 1990 to 2015. The basic as usual (BAU), policy tightening (PT) and market allocation (ML) scenarios are set to forecast the TCE and CEI. The results show that under the BAU scenario, China’s CEI reduction commitments in 2020 (40%–45%) can be achieved, but the commitment in 2030 (60%–65%) cannot be achieved. Under the PT and ML scenarios, China can achieve its CEI commitments in 2030, and the TCE will decrease gradually after reaching its peak in 2030. Under the existing macro development planning and policy intensity in China, there are still certain pressures to achieve CEI reduction targets. It is necessary to implement policy adjustment and market mechanism incentives for both energy supply and consumption, optimize power supply structure, promote electric energy substitution, and accelerate the construction of a unified national electricity market, carbon market, etc.

Optimizing the Development Area to take the lead in Peaks-based on Location Quotient Analysis

The China Optimized Development Area was officially released in the National Main Functional Area Plan in 2011. It is an important population and economically intensive area of China and has a huge impact on China’s future development. Under the background of international commitment and national planning to reduce carbon emission intensity, China’s optimized development area should play a leading role. Based on the location quotient analysis method, this paper analyzes the basis and prospects of optimizing the development area to take the lead from the industrial structure level, and draws the following conclusions. From the perspective of a single city, it compares with the overall industrial structure of the country and obtains optimization. Compared with the whole country, the development zone has a better industrial structure as the basis for carbon emission peaks. The optimized development zone should continue to carry forward its advantages in industrial structure and play a leading role in China’s 2030 carbon emission peak.

The role of stock market and banking sector development, and renewable energy consumption in carbon emissions: Insights from G-7 and N-11 countries

This study probes the role of disaggregated financial development and renewable energy in carbon emissions by incorporating gross fixed capital formation and economic growth in the function of carbon emissions. The financial development is measured through the stock market and banking sector development. We also examine the validity of the EKC hypothesis, using the data of G-7 and N-11 countries spanning from 1990 to 2016. The integration properties of the considered variables are examined through second generation unit roots tests. The Lagrange Multiplier (LM) bootstrap panel cointegration method has confirmed the long-run equilibrium relationship among the variables for all the four models used. The long-run elasticity results suggest that renewable energy increases environmental quality by reducing carbon emission intensity for both groups of panel countries. Banking development index decreases carbon emissions in G-7 countries, while increases carbon emissions in N-11 countries. Similarly, stock market development index increases carbon emissions in G-7 countries, while decreases in N-11 countries. Overall, economic growth and fixed capital formation impede environmental quality by accelerating the intensity of carbon emissions. This study suggests policy implications based on the empirical results for both groups of countries.

Influential factors of carbon emissions intensity in OECD countries: Evidence from symbolic regression

Carbon emissions intensity is used as a yardstick to evaluate the relative extent of carbon emissions of a country. To reduce carbon emissions intensity, it is crucial to find out the dominant factors that enhance it and take actions to manage them. This study is an attempt to employ symbolic regression method to explore the most influential factors of carbon emissions intensity in 34 OECD member countries using data from 1995 to 2014. Rooted in genetic programming, symbolic regression method can explore the inherent functional structures amidst the variables and could discover the influential factors robotically. The results confirm that the influential factors of carbon emissions intensity vary from country to country. GDP is found as the most frequent and important factor of carbon emissions intensity in 17 countries. Each of industrialization and technological innovation is the prominent factor in every 4 countries. And each of urbanization, total population, and foreign direct investment plays a pro-active role in 3 individual nations. Based on the empirical results, some policy implications are also conferred.

Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China

A rational regional allocation scheme of carbon emission permits is important for establishing the nationwide carbon emission trading system (ETS) of China. Considering the vast area and great regional differences in China, a novel bi-level allocation scheme based on clustering analysis and a weighted voting model is proposed in this research. At the first level, 30 regions in China are clustered into four classes according to their characteristics of carbon emissions, which include emission reduction pressure, capacity, responsibility and potential. Then the total carbon emission permits are allocated to each class of regions. At the second level, carbon emission permits are allocated to each province (municipality) within a class. The weighted voting models are developed for both the two levels, where the allocation schemes based on historical emissions, population and GDP are selected by each region according to the different voting rights of each region. The voting rights of each region for choosing its inclined scheme are quantified through a multi-index comprehensive evaluation, which employs the entropy method at the first level due to the lack of prior knowledge and the analytic hierarchy process (AHP) at the second level to increase policy flexibility. The combination of subjective weighting evaluation method (AHP) and objective weighting evaluation method (the entropy method) increases the flexibility of the abatement policy while guaranteeing the objectivity of decision-making process. Case studies of the allocation for carbon emission permits in China by 2020 and 2030 are carried out under the proposed allocation scheme. The derived allocation results show that the proposed allocation scheme can provide a balanced consideration to equality and efficiency, which are compared with those in reported literatures. The proposed allocation scheme can not only encourage all regions to reduce carbon emission intensity, but also achieve in meeting the carbon emission demand of the population in each region.