A Newsvendor Non-Cooperative Game for Efficient Allocation of Carbon Emissions

Responsible production policies with substitution and carbon emissions trading

An integrated zero-sum game and data envelopment analysis model for efficiency analysis and regional carbon emission allocation

Analysis of driving factors and allocation of carbon emission allowance in China

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

To establish the carbon emission trading scheme and achieve the carbon emission reduction goals in China, it is critical to allocate the carbon emission allowance (CEA). Using the entropy method and the modified fixed cost allocation model (MFCAM), we calculated the CEA and the carbon emission intensity (CEI) reduction targets of 30 Chinese provinces in 2030, from four principles (equity-efficiency-feasibility-sustainability) and three dimensions (economy-society-environment). The results are shown as follows. First, China's total carbon emissions in 2030 calculated in this paper are 17567.9 Mt. Second, on the whole, CEA in China's southeast half of the Hu line is higher than that in the northwest half. Eastern China has a larger final CEA than western China and central China. Third, Henan, Guangdong, Shandong, and Jiangsu are the four provinces with the most CEA, while Gansu, Qinghai, Ningxia, and Hainan are the four regions with the least carbon allowances. Fourth, the regions of Shanxi, Shaanxi, Xinjiang, Ningxia, Inner Mongolia, Guizhou, and Anhui will take on greater responsibility for carbon reduction in the future. On the contrary, the zones of Tianjin, Qinghai, Guangxi, Jilin, Yunnan, and Beijing will be able to sell CEA in the future. Fifth, provinces are divided into three categories from the perspective of CEI reduction. Finally, we put forward relevant policy recommendations based on the conclusions.

This study examines the nexus between business strategy and carbon emissions by utilising a dataset of U.S. firms from 2007 to 2020. It focuses on two broad types of firms, that is, prospectors and defenders. Regarding carbon emissions, we consider total emissions (Scope 1 & 2), direct emissions (Scope 1) and indirect emissions (Scope 2). The results reveal a significant association between business strategy and total carbon emissions as well as direct carbon emissions. Notably, the results suggest that prospectors, compared to defenders, display higher levels of total and direct carbon emissions. Our findings contribute to the debate on whether prospectors in developed countries mismanage sustainability issues. The study offers valuable insights into the interplay between business strategy and carbon emissions and provides empirical evidence that business strategy is an important determinant of total and direct carbon emissions.

Effectively exploring the impacts of urban spatial structures on carbon dioxide emissions is important for achieving low-carbon goals. However, most previous studies have examined the impact of urban spatial structure on total carbon emissions based only on polycentricity. Fine-grained studies on subsectoral carbon emissions and other dimensions of urban spatial structure are lacking. Therefore, our study comprehensively explores the impact of urban dispersion and polycentricity on total carbon emissions and carbon emissions of four subsectors (industry, power, civilian, and transportation) from 2012 to 2017 while considering the effects of city size. Results reveal that the nighttime light data is useful for measuring urban spatial structure, and a polycentric, decentralized urban spatial structure correlates with the reduced total carbon emissions and transportation carbon emissions. Meanwhile, a decentralized urban spatial structure gives rise to lower industrial carbon emissions and civilian carbon emissions, whereas a multicenter urban spatial structure contributes to minimizing carbon emissions from power systems. However, in small and medium-sized cities, urban spatial structure differently affects the total carbon and transportation carbon emissions.

In order to achieve its 2030 carbon emission peak target, China needs to adjust and allocate energy consumption and initial carbon emission allowances for each province in a phased and planned manner. Thus, this study applied an improved dynamic undesirable zero-sum-gains slacks-based-measure (ZSG-SBM) model to evaluate provincial CO2 emission reduction scenarios and energy allocation for 2015–2019 and calculate the optimal allocation values of carbon emission allowances for each province in 2030. The results showed that China’s allocation efficiency values for total energy exhibited rising and then declining trends during 2015–2019 and that most input–output term efficiency values had room for improvement. Furthermore, after four adjustment iterations of the improved dynamic undesirable ZSG-SBM model, the modeled China achieved optimal carbon emission efficiency for the whole country. In the final model, 19 provinces were allowed to increase their carbon emissions in 2030, while the remaining 11 provinces needed to reduce their emissions. The findings of this paper can help regulators to establish fairer and more effective policy solutions to promote regional synergistic emission reduction, achieve the national goal of peak total carbon emissions, and promote the green, coordinated, and sustainable development of China’s economy.

Carbon inequality in residential buildings: Evidence from 321 Chinese cities

China's forest carbon sinks and mitigation potential from carbon sequestration trading perspective

IntroductionPopulation expansion and economic development increased global greenhouse gas emissions, leading to serious environmental degradation. China, the world's largest developing country and promoter of the “Belt and Road Initiative” (BRI), accounts for 28.8% of the world"s total energy carbon emissions. How to reduce energy consumption to achieve the “double carbon” target (i.e., carbon peaking and carbon neutrality) and promote the implementation of Green BRI is still a serious challenge that China needs to face. MethodsWe evaluated China's carbon emissions using three indicators (i.e., total carbon emission, carbon intensity, and carbon emissions effect), and used spatial analysis to reveal the spatial and temporal trends of China's carbon emissions. In addition, the LMDI model was adopted to explore the driving mechanism of carbon emissions, so as to seek a path that can achieve harmonious economic and environmental development, as well as the “double carbon” target.ResultsChina's total carbon emission increased at a rate of 226.12% from 2000 to 2019, while the carbon intensity decreased at a rate of 48.84%. Carbon emission showed a trend of increasing and then decreasing from southwest to northeast. From 2000 to 2019, the total carbon emission, Gross Domestic Product (GDP), population size and total energy consumption are growing in synergy. Economic and population effects are positively related to carbon emissions, while technology effects are negatively related to it, indicating technological innovations contribute to the reduction of carbon emissions.DiscussionSome suggestions were proposed to control carbon emissions with a view to helping policy makers to formulate relevant policies. The findings provide a scientific basis and reference for the country to achieve the “double carbon” target and the low-carbon sustainable development of BRI.

Global Warming Due to Carbon Emissions is a threat to life on earth. According to the World Meteorological Organization, the earth's temperature rose by 1.06 °C to 1.26 °C above pre-industrial levels (1850–1900). According to the IPCC report (2021), the impacts of climate change will reach all regions in the world without exception. Indonesia's commitment to reduce carbon emissions by 29% with its efforts and 41% with international cooperation until 2030. Analysis of land cover uses the Unsupervised Classification, the method for calculating carbon emissions above the surface uses the IPCC method, namely the stock differential and the method for analyzing emissions of peat decomposition uses the Hooijer formula et al. (2006, 2010) with estimated water level from the TMAT BRGM data regression equation with NDWI Landsat 8 values then total carbon emissions by adding emissions from above and below the surface. Based on the results of the analysis of land cover at the research location, the area was dominated by plantations at 37.8%, while the forest area was only 7.9%. Ground carbon emissions average 0.23 Mt CO2-eq per year, below Ground carbon emissions are 9.94 Mt CO2-eq and total carbon emissions are 10.17 Mt CO2-eq with total emissions from 2013 - 2022 of 91.6 Mt CO2-eq Carbon emissions in KHG Sungai Mendahara - Batanghari graphically fluctuate, there is an increase and decrease but tends to increase until 2022 this is due to land conversion and massive land clearing in forest areas and the biggest contributor to carbon emissions from decomposition peatlands.

The 3D sand printing (3DSP) technology provides a richer realization path for the sustainable development of the manufacturing industry. It has the advantages of one-time molding, reducing design constraints and machining amount, and easy control of casting dimensional accuracy. Therefore, studying the impact of process parameters of the printing process on printing efficiency, resource consumption, and carbon emissions is the basis for the sustainable development of 3DSP technology. In this paper, starting from the main influencing parameters such as printing layer thickness, recoater speed, printing angle, and single printing quantity, a relationship model between printing parameters and carbon emission sources is constructed. A total factor carbon emission prediction model of 3DSP process including the impact of capital and labor is established. Build an influence relationship with printing parameters as independent variables and carbon emissions as dependent variables. Taking the sand casting industry as an example to verify the above model, the experimental results show that the thickness of the printing layer has the greatest impact on carbon emissions. When the layer thickness is 0.36 mm, the speed of the recoater is 0.22 m/s, the printing angle is (0°, 0°, 90°), and the single print quantity is 84, the total carbon emission is the lowest and 28.77% less compared to the parameter with the highest carbon emission. The average relative error of the predictive model is 1.929%. The results of this study can provide some new ideas for sustainable development of additive manufacturing technology.

Civil construction is an important source of carbon emissions. The implementation of total carbon emission control for civil buildings is an important means to achieve energy conservation and emission reduction in China. The study divides civil buildings into urban residential buildings, rural residential buildings, public buildings, and northern urban heating. The study calculates and predicts the total carbon emissions of China’s civil buildings. Firstly, according to the statistical method of IPCC, the calculation model of carbon emissions of civil buildings is constructed, and the total amount of carbon emissions of civil buildings in China from 2005 to 2016 is calculated by using the energy balance table. Further, based on the improved grey prediction method, the national civil use from 2017 to 2025 is predicted. Total carbon emissions from construction. The forecast results show that in 2017-2025, the annual average carbon emission growth rate of buildings is about 4.65%, and by 2025, the total emission is expected to reach 298.176 million tons of CO2. In order to achieve the 2030 emission reduction target, it is necessary to further optimize the energy structure, improve the energy efficiency of the building, and increase the carbon emission reduction in civil construction.

This study takes Kunming City, Yunnan Province, China as the research area, to provide reference basis for revealing the change law of land use structure and energy consumption and carbon emissions in Kunming, optimizing land use structure and realizing the development of low-carbon city. Based on the data of land use structure and energy consumption in Kunming from 1997 to 2017, based on the estimation of total energy consumption carbon emissions, carbon intensity and per capita carbon emissions, the correlation between land use structure and energy consumption carbon emissions in Kunming has been calculated and analyzed in the past 20 years. Results: 1) The total amount of carbon emissions in Kunming has increased significantly in the past 20 years. It increased from 34.46 × 105 t to 95.09 × 105 t, an increase of about 2.8 times. 2) The types of land use with the highest correlation between land use structure and total carbon emissions of energy consumption, carbon emission intensity and per capita carbon emissions are urban and village and industrial and mining land (0.8258), cultivated land (0.8733) and garden land (0.7971) respectively. 3) The correlation between construction land and total carbon emissions is greater than that of agricultural land. Conclusion: There is a close correlation between land use structure and carbon emissions from energy consumption in Kunming.