Factors Affecting Carbon Emissions Research Articles

Corporate governance and carbon emissions performance: International evidence on curvilinear relationships

This study investigates the impact of corporate governance mechanisms (namely board meeting, board independence, board gender diversity, CEO duality, ESG-based compensation and ESG committee) on carbon emissions performance of multinational entities (MNEs). The study analysed international sample of 336 top MNEs operating in 42 non-financial industries from 32 countries over a 15-year period. Result shows that board gender diversity, CEO duality, and ESG committee are negatively associated with carbon emissions rate, whilst board independence and ESG-based compensation have significant positive impact. Whereas board gender diversity and CEO duality have significant negative impact on carbon emissions rate in carbon-intensive industries, the impact of board meeting, board independence and ESG-based compensation is significant and positive. In the non-carbon-intensive industries, board meeting, board gender diversity and CEO duality have significant negative impact on carbon emissions rate, whilst the impact of ESG-based compensation is positive. Further, there is a negative association between the millennium development goals (MDGs)/sustainable development goals (SDGs) era dichotomy and carbon emissions rate, implying that the United Nations agenda for sustainable development significantly affected carbon emissions performance of MNEs, with the SDGs era generally witnessing better carbon emissions management in comparison to the MDGs era in spite of the higher emissions level in the SDGs era. The study contributes to knowledge in several ways. First, it adds to the limited literature on the determinants of carbon emissions reduction within an international context. Second, the study addresses mixed result reported in prior studies. Third, the study adds to knowledge on the governance factors affecting carbon emissions performance in the MDGs and SDGs periods, thus providing evidence on progress MNEs are making towards addressing climate change challenges through carbon emissions management.

Scenario Prediction of Carbon Emission Peak of Urban Residential Buildings in China’s Coastal Region: A Case of Fujian Province

With the acceleration of China’s urbanization process, the importance of energy conservation and emission reduction in the building sector has become increasingly prominent. The effective control of carbon emissions in coastal provinces has a decisive impact on achieving the carbon emissions peak target nationwide. Based on the analysis of the influencing factors, this study establishes an urban residential buildings carbon emission prediction model by combining the IPAT model and the ridge regression model. In addition, the prediction model is combined with scenario analysis to simulate the evolution of carbon emission trends of urban residential buildings in Fujian Province from 2018 to 2050 under different scenarios. The results show that total population, urban living area, residents’ consumption expenditure, urbanization rate, per capita GDP, and energy structure are key factors affecting carbon emissions from urban residential buildings in coastal cities. Only under the ultra-low carbon model scenario can Fujian’s urban residential buildings achieve the carbon peak goal in 2027 (13.4748 million tons of CO2), which requires a reduction of 59.67% compared to that under the baseline model scenario. This study can provide an effective reference for energy conservation and emission reduction work of the regional scale and even the national scale.

Research on Carbon Reduction Paths in Recycling Industrial Parks based on System Dynamics

The “double carbon” target is a medium-to-long-term national strategy proposed by China to combat climate change. The industrial sector is one of the key areas for the implementation of the “double carbon” target. Therefore, studying the association between carbon emission factors and carbon emissions is crucial to reduce greenhouse gas emissions from industrial activities. In the present study, the association between factors affecting carbon emissions and carbon emissions in a circular economy were investigated for an industrial park in Northwest China. A carbon emission system dynamics model for this circular economy industrial park was constructed, in reference to the relevant national policies and the current condition of the park. Five different scenarios were utilized to dynamically simulate the impact of rapid economic development, energy restructuring, industrial restructuring, and technological development, and carbon emission reduction paths for industrial parks were explored. The results showed that, the park would peak at 1134.67 thousand tons of CO2 in 2032, according to the baseline scenario, with industrial energy consumption accounting for over 80% of the total emissions. A combined regulation scenario, with increased investment in research and development and environmental management, would achieve a peak in 2030, with a relatively lower peak of 1062.88 thousand tons of CO2. Our findings provides new insights into the paths of carbon emission reduction in recycling industrial parks.

Regional impact of aging population on carbon dioxide emissions in China: Evidence from panel threshold regression (PTR).

Carbon dioxide emission is a high-profile issue that can affect both the human economy and human existence, but few scholars have studied the relationship between these two. Therefore, this study constructs the panel threshold regression (PTR) based on the National Bureau of Statistics of China's panel data from 2002 to 2019 in 19 regions. One of the advantages of PTR is to leverage segmented functions, allowing for a more detailed analysis of the data. Besides, by introducing the idea of a threshold, PTR can effectively avoid structural changes in the data. The different between this study and other research is that this study divides 19 regions into two parts: Eastern China and Central China. Based on that, this study researches and compares the different influences of the aging population on carbon emissions in these two regions. The results show that although the Environment Kuznets Curve has been confirmed in both Eastern China and Central China, with the development of the economy, the carbon emissions will increase in Eastern China and decrease in Central China, respectively. In addition, the key factors affecting carbon emissions in Eastern China and Central China are trade dependence and urbanization rate separately. Hence, this study suggests that the regional governments in Eastern China may guide and encourage more international trading companies to move to Central China, and at the same time, the regional governments in Central China should issue more policies to attract these companies, such as: reducing land lease fees and building better transportation infrastructure. Apart from that, the governments in Central China should vigorously increase the rate of urbanization to reduce energy consumption and improve energy efficiency.

Research on carbon emission factors of heavy chemical industry park based on generalized Divisia index method

Based on the energy consumption and economic data of a heavy chemical industrial park in northwest China from 2013 to 2020, the carbon emissions of the park were calculated. The generalized Divisia index method (GDIM) was employed to analyze the effects of eight absolute and ten relative indicators of carbon emission changes in the heavy chemical industrial park. The results show that optimizing the electricity structure, reducing the energy output and carbon output intensities, and improving investment efficiency have a significant effect on inhibiting carbon emission. The inhibitory effect from reducing investment carbon intensity, fossil energy, and the total energy consumption scale was clearly evident. The deterioration of fossil fuel energy structure, and the increase in investment, output, and total electricity consumption scales are the main factors affecting carbon emission growth. Carbon emissions from industrial processes have a limited impact on carbon emissions. Finally, pertinent suggestions are provided.

Study on Contribution of Carbon Emission Intensity Based on Oaxaca-blinder Decomposition Method by Big Data Evaluation in Different Economic Level Cities of Shandong Province

We studied the difference in carbon emission intensity by the Oaxaca blinder decomposition method with the panel data of 17 prefecture level cities in Shandong Province from 2007 to 2016. Per capita GDP, industrial structure, urbanization level, and energy consumption intensity are important factors affecting carbon emission intensity. The increase in urbanization rate and energy consumption intensity leads to an increase in carbon emission intensity. The reduction of the proportion of secondary industry in low-level cities is conducive to the reduction of carbon emission intensity. At the same time, we selected factors from the economic, energy, and social indicators in the low-level city rating system to reveal the contribution of different factors to the difference in carbon emission intensity. This is helpful to narrow the gap among the cities, accelerate the transformation from low-carbon cities to non-carbon cities, and realize the economic development of low-level cities.

Measurement and Driving Factors of Carbon Emissions from Coal Consumption in China Based on the Kaya-LMDI Model

As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China’s actual development.

Path of carbon emission reduction through land use pattern optimization under future scenario of multi-objective coordination

Land use change is one of the crucial factors affecting carbon emissions. The continuously increasing CO2 and global warming have raised concerns about carbon emission reduction in the process of urbanization. In this research, the Markov and multi-objective optimization models were conducted to predict the demands for land use in Nanjing in 2030 and 2060 under the natural growth (NG) and minimum carbon emission (MCE) scenarios to coordinate the needs of economic development, ecological protection and food security as well as the target of carbon emissions reduction in the future. The spatial distribution of land use simulated by the FLUS (Future Land Use Simulation) model was used to evaluate the effects of future land use on carbon emissions. The results showed that 1) The demands for each type of land use in the NG scenario were significantly different from those in the MCE scenario. Considering the goals of food security and ecological protection in the future, the total amount of cultivated land would not decrease in the MCE scenario, and the area of construction land was significantly smaller than that in the NG scenario. 2) The carbon emissions of Nanjing under the MCE scenario would decrease by 3.94 and 11.80 million tons in 2030 and 2060, respectively, accounting for 9.97% and 27.17% of the total carbon emissions. The optimization of land use patterns can effectively reduce carbon emissions in the process of urbanization.

Industrial Structure Upgrading, Technological Progress, and Regional Carbon Emission Intensity

At present, China is in a critical period of transformation from high-speed development to high-quality development.Energy conservation and emission reduction conform to the concept of high-quality development in China. The upgrading of industrial structure and technological progress are important factors affecting carbon emission intensity. Based on the provincial panel data from 2010 to 2019, this paper explores the relationship between industrial structure upgrading, technological progress and regional carbon emission intensity from both theoretical and empirical aspects by building a mediation effect model. The results show that the upgrading of industrial structure has a significant inhibitory effect on carbon emission intensity; Technological progress has a significant inhibitory effect on carbon emission intensity; Technological progress plays a part of intermediary effect between industrial structure upgrading and carbon emission intensity.Finally, it puts forward policy suggestions such as reasonably promoting industrial structure adjustment, promoting industrial structure optimization, establishing an effective mechanism to promote technological progress, and tackling environmental pollution from the source.

The impact of international technical cooperation in new energy industry on carbon emissions: evidence from the top 30 countries in the global innovation index.

International cooperation has become a global consensus to solve environmental problems. This paper selects the top 30 countries in the global innovation index as the research sample, based on Patent Cooperation Treaties (PCT) data jointly applied in the field of new energy, constructs the international technical cooperation network, and uses the fixed effect panel regression model to verify the influence of international technical cooperation of new energy industry on carbon emission intensity. The results show that the USA, Germany, the UK, France, and the Netherlands are at the center of the network. International technological cooperation in new energy industry has a significant negative impact on carbon emission intensity. The convening of the United Nations Climate Change Conference in Copenhagenhas accelerated global industrial upgrading, and the effect of international technical cooperation in new energy on carbon emission reduction has been strengthened. In addition, the level of economic development, international trade, and research and development (R&D) are also important factors affecting carbon emission intensity. Countries with high network centrality should give full play to their network influence to promote global cooperation in the field of new energy and achieve carbon mitigation targets by signing more environmental agreements.

Market incentives, carbon quota allocation and carbon emission reduction: Evidence from China's carbon trading pilot policy.

As a major carbon dioxide-emitting country, China set carbon trading market to reduce enterprise carbon emissions through the rational allocation of carbon quotas among different enterprises and regions. The market has also conducted a preliminary exploration for the country to achieve carbon dioxide emissions peak in 2030 and carbon neutrality in 2060 while actively addressing the challenges of global climate change. This study analysed the emission reduction effect of China's carbon trading pilot policy, especially the role of carbon quota and carbon trading price. The analysis used county-level panel data from 1997 to 2017, regarded the implementation of the carbon trading pilot policy as a quasi-natural experiment, and used the difference-in-differences method. The results showed that, first, the policy implementation not only reduced regional carbon emissions but also inhibited carbon dioxide emissions per capita, with long-term effects. Second, the carbon emission reduction effect brought by the carbon pilot policy showed significant heterogeneous results with the different degrees of regional carbon emissions and environmental supervision. The effect was greater in areas with higher carbon emission density and stronger legal supervision. Third, the difference in carbon quota allocations resulted in different emission reduction effects, among which the historical method had the strongest effect. The carbon quota price and number of enterprises participating carbon trading market were the key factors affecting carbon emission reduction.

Spatial-temporal characteristics and influencing factors of county-level carbon emissions in Zhejiang Province, China.

Zhejiang Province is a "demonstration area for high-quality development and construction of common prosperity" in China. Moreover, the county is the basic unit and power source for the economic development of Zhejiang Province. Therefore, the research on the spatial-temporal characteristics and influencing factors of county-level carbon emissions is of great significance for Zhejiang Province to achieve the strategic goal of carbon peak and carbon neutrality. Based on the carbon emissions and socio-economic data of 62 counties in Zhejiang Province from 2014 to 2020, the spatial dependence and agglomeration of county-level carbon emissions are analyzed through the spatial autocorrelation test and local spatial autocorrelation test respectively. According to the spatial-temporal characteristics of county-level carbon emissions revealed by the index of Moran's I and local Moran's I, the spatial error STIRPAT model is used to study the influencing factors of county-level carbon emissions in Zhejiang Province, China. The main results are as follows: (1) The total amount of county-level carbon emissions of 62 counties fluctuates from 259.69 to 326.28 million tons and shows a growth trend. (2) Moran's I index is between 0.369 and 0.399. The county-level carbon emissions have a significant spatial correlation, and the spatial agglomeration trend is relatively stable, which is consistent with the hypothesis of the geographical polarization effect. (3) High-high agglomeration counties are concentrated in the northeast of Zhejiang Province, while low-low agglomeration counties are mainly in the southwest. (4) The relationship between county per capita GDP and carbon emissions has not been "decoupled," because when other variables remain unchanged, the county's total carbon emissions will increase by 2.866% for every 1% increase in the county's per capita GDP; the increase of the proportion of secondary industry contributes to the decline of carbon emissions, and the low-carbon effect brought by large-scale industrial development as well as scientific and technological innovation has not yet appeared. (5) The estimate of the spatial coefficient λ was 0.324, which illustrates that the carbon emission of a single county is positively affected by the carbon emission of the neighboring counties, and other socio-economic factors affecting carbon emission among counties also have a spatial correlation. Therefore, the policy of realizing regional coordinated development as well as the carbon peaking and carbon neutrality goals should not only focus on industrial layout, but also take a dynamic and comprehensive consideration from a spatial perspective.

Carbon performance evaluation system and practice analysis for the sustainable enterprises

AbstractSustainable development has become society's focal point. In this context, China's paper industry needs to be more concerned about a low‐carbon strategy. Therefore, a carbon performance evaluation system must be built to guide the low‐carbon enterprise transformation. Based on the carbon value flow analysis, this study takes JX Paper (a paper‐making enterprise) as an example to construct a carbon performance evaluation system consisting of carbon input, transfer, and output indicators. The quantitative and qualitative index weights were determined through a principal component analysis and an analytical hierarchy process. The overall carbon performance increased from 2012 to 2021, but room remains for improvement. These results show that the performance evaluation system provides a helpful reference for enterprises to identify the key factors affecting carbon emissions and evaluate their carbon performance. Moreover, objective criteria should be developed to improve sustainable enterprise carbon performance and low‐carbon competitiveness.

Strategies to Mitigate Carbon Emissions for Sustainable Aviation: A Critical Review From a Life-cycle Perspective

The international aviation industry is one of the fastest growing industries, resulting in an increase of greenhouse gas emissions. Controlling aviation carbon emissions is needed to further prevent future climate change as the demand for aviation continues to expand. Effective ways should be taken to control aviation carbon emissions, namely identifying the set of driving factors behind the carbon emissions, as well as, exploring low-carbon technologies in aviation. However, few studies have systematically identified the driving factors and given a timeline list of aviation low-carbon technologies. Therefore, from the life cycle perspective (including the aircraft designing, operating, and recovery), this paper applies text mining and literature metrology to identify the driving factors systematically. Moreover, an evaluation model is established to present the prioritization of factors, and the Delphi method is applied to create a timeline list of the low-carbon technologies in the aviation industry. The results show that demand, technological improvement, and alternative fuels are the most important factors affecting carbon emissions in the aviation industry, with their contribution percentages of 16 %, 14 %, and 12 %, respectively. In the short term, aviation relies more on policies to reduce emissions (supported by 79 % of experts). In the long term, technological progress enables the production of increasingly mature alternative fuels as well as reduces production costs. Among the set of experts, 74 % support strategies relating to renewable fuel and low carbon technologies as the main factors for the aviation industry to reduce carbon emissions.

Towards carbon neutrality by improving carbon efficiency - A system-GMM dynamic panel analysis for 131 countries’ carbon efficiency

Given that improving carbon emission efficiency is the cost-effective measure to achieve carbon peak and carbon neutrality, achieving carbon peak/neutrality with minimal economic costs demands a more comprehensive understanding of the factors affecting carbon emission efficiency. In this work, a dynamic panel data analysis with generalized method of moments (GMM) estimation was developed to investigate the effects of industrialization, and renewable energy on carbon emission efficiency, to uncover the interactive and influence channels among these influencing factors using 131 countries’ panel data. The results show that: (i) although urbanization and population aging are not conducive to the improvement of carbon emission efficiency in higher-income countries, they are conducive to the improvement of carbon emission efficiency in lower-income countries. (ii) Trade openness in the global community will have a negative impact on carbon emission efficiency. However, trade openness plays a vital role in improving the carbon emission efficiency of high-income economies. (iii) The rise in renewable energy and industrialization has improved carbon emission efficiency, and the utilization and development of renewable energy in higher-income countries is generally higher than in lower-income countries, and the environmental governance system is relatively sound.

Life cycle assessment of carbon emission from natural gas pipelines

With the deterioration of global climate and environment, the carbon emission of the oil and gas industry has been an important issue of global concern. At present, there is a lack of systematic research on carbon emission calculation of natural gas pipeline from construction to disposal. We propose a life cycle assessment method for natural gas pipeline in order to quantify the carbon emissions from construction to disposal. The carbon emissions of natural gas pipeline are divided into four parts in this model: manufacturing, construction, operation, and recycling. For each of the four stages, carbon emissions from material production and construction, facility construction and equipment operation are all detailedly calculated. In addition, methane, the main component of natural gas, has a non-negligible impact on the atmosphere. Therefore, gas leakage from the pipeline system is also considered in this paper. And data from actual pipelines are utilized in the case study. The results show that the carbon emissions of these pipelines are in the range of 26.58–67.14 t CO2 / (km×108 m3 / a) and 11.41–30.27 t CH4 / (km×108 m3 / a). Among them, the production process of pipelines contributes the most to carbon emissions, accounting for about 80 % of the total CO2 emissions. Through sensitivity analysis, we found that pipeline parameter, power emission factor, and compressor operation status turn out to be the main factors affecting carbon emission. With the establishment of this model, potential carbon emission of planned pipelines can be estimated, which has guiding significance for future pipeline construction.

Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example

Driven by China’s peak carbon emissions and carbon neutrality goals, each region should choose a suitable local implementation path according to local conditions, so it is of great significance to mine and analyze the critical influencing factors of regional carbon emissions. Therefore, this paper integrates grey relation analysis (GRA) and an improved STIRPAT model and selects the Yangtze River Delta region of China as the research object to analyze the factors affecting carbon emissions in four provinces in the region. Firstly, it uses the IPCC method to calculate the energy carbon emissions of each province. Secondly, according to the existing research, the relevant influencing factors of carbon emissions are sorted and summarized as candidate sets and this paper uses GRA to calculate the correlation degree of the above candidate sets. On this basis, this paper combines with the characteristics of the improved STIRPAT model to determine the index selection criteria and filter out the critical factors of each province. Thirdly, an improved STIRPAT model is constructed for each province to explore the influence of critical factors and analyze the influencing factors of carbon emissions in detail. The empirical results show that during the period from 2005 to 2019, the carbon emissions of the four provinces in the Yangtze River Delta are significantly different in structure and trend. At the same time, the critical influencing factors of each province are different and the influence of the same factor on different regions is significantly different. Finally, the policy suggestions for the provinces to achieve their peak carbon emissions and carbon neutrality goals are precisely tailored to the different carbon emission influencing factors.

Building a novel multivariate nonlinear MGM(1,m,N|γ) model to forecast carbon emissions.

With the proposal of the carbon neutrality target, China's attention to carbon emissions has been further enhanced. Effective prediction of future carbon emissions is important for the formulation of carbon neutralization target and action plans in the region. Many factors affecting carbon emissions, cause their development trends may be nonlinear. To forecast the carbon emissions of coal and natural gas in the industrial sector more accurately, a new MGM(1,m,N|γ) model considering nonlinear characteristics is proposed in this paper. The new model introduces power function γ as nonlinear parameter, and the γ value is solved by nonlinear constraint function. We further deduce the simulation and prediction formula and then apply the improved model to the carbon emission forecast. The comparisons show that the nonlinear parameters can modify the trend of sequences and improve the prediction accuracy, which verifies the validity of the model. Finally, according to the influencing factors and forecast results, this paper analyzes the causes of high carbon emissions and puts forward reasonable suggestions for China's carbon governance.

Decomposition and Decoupling Analysis of Factors Affecting Carbon Emissions in China’s Regional Logistics Industry

This paper selected the data from 2010 to 2020 to measure the carbon emissions of the logistics industry in different regions of China, decomposed the influencing factors of carbon emissions in China’s logistics industry based on the LMDI model, and, finally, conducted a decoupling analysis of carbon emissions and the development of the logistics industry. The conclusions are as follows: (1) China’s carbon emission levels vary greatly from region to region, with the highest distribution pattern in the east and the lowest in the west, while the growth rate in the east is also the highest. (2) The level of economic development has the greatest impact on carbon emissions, and it has the effect of promoting carbon emissions in three regions; logistics development effects have the characteristics of first driving and then restraining emissions in the three major regions. The effect of energy intensity has great volatility. The effect of intensity in the eastern region dropped sharply in 2015, with negative effects after that year. Development of the logistics industry has limited the inhibition of carbon emissions in the central and western regions. Although the effect of the energy structure is negative, it failed the significance test. The effects of the energy structure began to show a downward trend in three regions after 2015. (3) The decoupling analysis showed that only 3 provinces are strongly decoupled, 20 provinces are weakly decoupled, and the regional carbon emissions are quite different.

Error Correction Model: Factors Affecting Carbon Emissions in Supporting Sustainable Development in Indonesia

This study is a study that aims to determine the effect of population, economic growth, and consumption of renewable energy on CO2 emissions in Indonesia. The type of data used in this study is secondary data and is time-series data from 1990-to 2019. The method of analysis in this study is the Error Correction Model (ECM) which aims to determine the existence of short-term and long-term relationships between the analyzed variables. Based on the results of data analysis conducted in this study, it can be concluded that in the short term population and consumption of renewable energy have no effect on CO2 emissions in Indonesia, while economic growth has a positive effect on CO2 emissions in Indonesia. In the long term, the results obtained are that population and economic growth have a positive effect on CO2 emissions in Indonesia, while consumption of renewable energy has no effect on CO2 emissions in Indonesia.