Analysis of Carbon Emissions and Their Influence Factors Based on Data from Anhui of China

Global warming and world-wide climate change caused by increasing carbon emissions have attracted a widespread public attention, while anthropogenic activities account for most of these problems generated in the social economy. In order to comprehensively measure the levels of carbon emissions and carbon sinks in Anhui Province, the study adopted some specific carbon accounting methods to analyze and explore datasets from the following suggested five carbon emission sources of energy consumption, food consumption, cultivated land, ruminants and waste, and three carbon sink sources of forest, grassland and crops to compile the carbon emission inventory in Anhui Province. Based on the compiled carbon emission inventory, carbon emissions and carbon sink capacity were calculated from 2000 to 2019 in Anhui Province, China. Combined with ridge regression and scenario analysis, the STIRPAT model was used to evaluate and predict the regional carbon emission from 2020 to 2040 to explore the provincial low-carbon development pathways, and carbon emissions of various industrial sectors were systematically compared and analyzed. Results showed that carbon emissions increased rapidly from 2000 to 2019 and regional energy consumption was the primary source of carbon emissions in Anhui Province. There were significant differences found in the increasing carbon emissions among various industries. The consumption proportion of coal in the provincial energy consumption continued to decline, while the consumption of oil and electricity proceeded to increase. Furthermore, there were significant differences among different urban and rural energy structures, and the carbon emissions from waste incineration were increasing. Additionally, there is an inverted "U"-shape curve of correlation between carbon emission and economic development in line with the environmental Kuznets curve, whereas it indicated a "positive U"-shaped curve of correlation between carbon emission and urbanization rate. The local government should strengthen environmental governance, actively promote industrial transformation, and increase the proportion of clean energy in the energy production and consumption structures in Anhui Province. These also suggested a great potential of emission reduction with carbon sink in Anhui Province.

A low-carbon economy is the most important requirement to realize high-quality integrated development of the Yangtze River Delta. Utilizing the following models: a super-efficiency slacks-based measure model, a spatio-temporal correlation model, a bivariate LISA model, a spatial econometric model, and a geographically weighted random forest model, this study measured urban industrial eco-efficiency (IEE) and then analyzed its influencing effects on carbon emission in the Yangtze River Delta from 2000 to 2017. The influencing factors included spatio-temporal correlation intensity, spatio-temporal association type, direct and indirect impacts, and local importance impacts. Findings showed that: (1) The temporal correlation intensity between IEE and scale efficiency (SE) and carbon emissions exhibited an inverted V-shaped variation trend, while the temporal correlation intensity between pure technical efficiency (PTE) and carbon emissions exhibited a W-shaped fluctuation trend. The negative spatial correlation between IEE and carbon emissions was mainly distributed in the developed cities of the delta, while the positive correlation was mainly distributed in central Anhui Province and Yancheng and Taizhou cities. The spatial correlation between PTE and carbon emissions exhibited a spatial pattern of being higher in the central part of the delta and lower in the northern and southern parts. The negative spatial correlation between SE and carbon emissions was mainly clustered in Zhejiang Province and scattered in Jiangsu and Anhui provinces, with the cities with positive correlations being concentrated around two locations: the junction of Anhui and Jiangsu provinces, and within central Jiangsu Province. (2) The direct and indirect effects of IEE on carbon emissions were significantly negative, indicating that IEE contributed to reducing carbon emissions. The direct impact of PTE on carbon emissions was also significantly negative, while its indirect effect was insignificant. Both the direct and indirect effects of SE on carbon emissions were significantly negative. (3) It was found that the positive effect of IEE was more likely to alleviate the increase in carbon emissions in northern Anhui City. Further, PTE was more conducive to reducing the increase in carbon emissions in northwestern Anhui City, southern Zhejiang City, and in other cities including Changzhou and Wuxi. Finally, it was found that SE played a relatively important role in reducing the increase in carbon emissions only in four cities: Changzhou, Suqian, Lu’an, and Wenzhou.

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies.

Estimating carbon emissions and assessing their contribution are critical steps toward China’s objective of reaching a “carbon peak” in 2030 and “carbon neutrality” in 2060. This paper selects relevant statistical data on carbon emissions from 2000 to 2018, combines the emission coefficient method and the Logarithmic Mean Divisia Index model (LMDI) to calculate carbon emissions, and analyses the driving force of carbon emission growth using Henan Province as a case study. Based on the partial least squares regression analysis model (PLS), the contributions of inter-provincial factors of carbon emission are analyzed. Finally, a county-level downscaling estimation model of carbon emission is further formulated to analyze the temporal and spatial distribution of carbon emissions and their evolution. The research results show that: 1) The effect of energy intensity is responsible for 82 percent of the increase in carbon emissions, whereas the effect of industrial structure is responsible for -8 percent of the increase in carbon emissions. 2) The proportion of secondary industry and energy intensity, which are 1.64 and 0.82, respectively, have the most evident explanatory effect on total carbon emissions; 3). Carbon emissions vary widely among counties, with high emissions in the central and northern regions and low emissions in the southern. However, their carbon emissions have constantly decreased over time. 4) The number of high-emission counties, their carbon emissions, and the degree of their discrepancies are gradually reduced. The findings serve as a foundation for relevant agencies to gain a macro-level understanding of the industrial landscape and to investigate the feasibility of carbon emission reduction programs.

Carbon emission is related to global ecological security, and economic development inevitably leads to an increase in carbon emission. In recent years, as a rapidly developing province in China's economy, Anhui Province has experienced significant differences in the spatiotemporal distribution of carbon emission in different regions due to differences in development foundation, urbanization level, population size, industrial structure, etc., providing representative empirical cases for research. Based on the carbon emission data of Anhui Province before the COVID-19, this study used exploratory spatial data analysis method and Geodetector to analyze the spatial and temporal distribution characteristics and drivers of carbon emission in Anhui Province. The study found that (1) the spatial differentiation and spatial correlation of carbon emission in Anhui Province are significant, At the beginning, it shows the characteristics of "high north and low south" and "high west and low east", and then the "core–edge" structure of carbon emission becomes obvious. Carbon emission hotspot areas increase and then decrease, mainly in Hefei, Fuyang and Chuzhou City, etc. The coldspot areas are mainly located in the southern and western mountainous areas, and the degree of aggregation is decreasing year by year. (3) The level of urbanization, economic development and population size are the main driving factors of the spatial variation of carbon emissions, while the industrial structure has the least influence. And most factors produce nonlinear enhancement when spatially superimposed with other factors. (4) The high value areas of economic development, population density, secondary industry structure, and energy intensity are all at high levels of carbon emission, and a combination of factors leads to the creation of high risk areas for carbon emission. The study provides a basis for reducing carbon emission in the next stage of Anhui Province, focusing on key carbon emission areas, and sustainable development.

Structural decomposition analysis of global carbon emissions: The contributions of domestic and international input changes

The urban agglomerations in the middle reaches of the Yangtze River (MYR-UA) are facing a severe challenge in reducing carbon emissions while maintaining stable economic growth and prioritizing ecological protection. The energy consumption related to land urbanization makes an important contribution to the increase in carbon emissions. In this study, an IPAT/Kaya identity model is used to understand how land urbanization affected carbon emissions in Wuhan, Changsha, and Nanchang, the three major cities in the middle reaches of the Yangtze River, from 2000 to 2017. Following the core idea of the Kaya identity model, sources of carbon emissions are decomposed into eight factors: urban expansion, economic level, industrialization, population structure, land use, population density, energy intensity, and carbon emission intensity. Furthermore, using the Logarithmic Mean Divisia Index (LMDI), we analyze how the different time periods and time series driving forces, especially land urbanization, affect regional carbon emissions. The results indicate that the total area of construction land and the total carbon emissions increased from 2000 to 2017, whereas the growth in carbon emissions decreased later in the period. Energy intensity is the biggest factor in restraining carbon emissions, followed by population density. Urban expansion is more significant than economic growth in promoting carbon emissions, especially in Nanchang. In contrast, the carbon emission intensity has little influence on carbon emissions. Changes in population structure, industrial level, and land use vary regionally and temporally over the different time period.

Global increasing carbon emissions have triggered a series of environmental problems and greatly affected the production and living of human beings. This study estimated carbon emissions from land use change in the Beijing-Tianjin-Hebei region during 1990–2020 with the carbon emission model and explored major influencing factors of carbon emissions with the Logarithmic Mean Divisia Index (LMDI) model. The results suggested that the cropland decreased most significantly, while the built-up area increased significantly due to accelerated urbanization. The total carbon emissions in the study area increased remarkably from 112.86 million tons in 1990 to 525.30 million tons in 2020, and the built-up area was the main carbon source, of which the carbon emissions increased by 370.37%. Forest land accounted for 83.58–89.56% of the total carbon absorption but still failed to offset the carbon emission of the built-up area. Carbon emissions were influenced by various factors, and the results of this study suggested that the gross domestic product (GDP) per capita contributed most to the increase of carbon emissions in the study area, resulting in a cumulative increase of carbon emissions by 9.48 million tons, followed by the land use structure, carbon emission intensity per unit of land, and population size. By contrast, the land use intensity per unit of GDP had a restraining effect on carbon emissions, making the cumulative carbon emissions decrease by 103.26 million tons. This study accurately revealed the variation of net carbon emissions from land use change and the effects of influencing factors of carbon emissions from land use change in the Beijing-Tianjin-Hebei region, which can provide a firm scientific basis for improving the regional land use planning and for promoting the low-carbon economic development of the Beijing-Tianjin-Hebei region.

In order to achieve the goal of reaching carbon peak by 2030, the STIRPAT model is used to predict carbon emissions under three simulation scenarios: baseline, optimization, and strict control of carbon emissions. Taking Anhui Province as an example, fully considering the impact of factors such as population, per capita GDP, carbon emission intensity, energy consumption intensity, energy structure, and industrial structure on carbon emissions, ridge regression and partial least squares regression were conducted respectively. Finally, the partial least squares regression method with a lower average error rate was selected to predict the model coefficients. The results show that all three model scenarios can achieve the carbon peak target by 2030, and the factors that have the greatest impact on carbon emissions are carbon emission intensity, energy consumption intensity, and per capita GDP.

With the accelerated development of the aging trend in Chinese society, the aging problem has become one of the key factors affecting sustainable economic and social development. Given the importance of controlling carbon emissions for achieving global climate goals and China's economic transformation, studying the spatial and temporal effects of population aging on carbon emissions and their pathways of action is of great significance for formulating low-carbon development strategies adapted to an aging society. This paper aims to explore the spatial-temporal effects of population aging on carbon emissions, identify the key pathways through which aging affects carbon emissions, and further explore the variability of these effects across different regions. The findings will provide theoretical support and empirical evidence for government departments to formulate policies to promote the coordinated development of a low-carbon society and an aging society. Based on the panel data of 30 provinces in China from 2004 to 2022, this paper systematically investigates the impact of population aging on carbon emission intensity from both spatial and temporal dimensions by using the spatial Durbin model and the mediating effect model. The direct effect of aging on carbon emission intensity, the spatial spillover effect, and the indirect effect through mediating variables such as residents' consumption, environmental regulation, and new urbanization are analyzed in depth. The study found that population aging in China has significant spatial and temporal effects on carbon emissions. From the spatial dimension, there is a significant spatial spillover effect of the effect of aging on carbon emissions, and aging reduces local carbon emissions but increases carbon emissions in adjacent regions. From the time dimension, the effect of aging on carbon emissions shows a stage characteristic, initially it will reduce carbon emissions, but with the deepening of aging, its effect may tend to weaken. In addition, this study identifies a number of key pathways through which aging affects carbon emissions, including reducing residential consumption, promoting new urbanization, and increasing the intensity of environmental regulations. Finally, this study explores the regional heterogeneity of the impact of aging on carbon emissions and its mechanism of action. This study is instructive: first, the complex impact of population aging on carbon emissions should be fully recognized to formulate a comprehensive low-carbon development strategy; second, attention should be paid to the spatial spillover effect of aging on carbon emissions to strengthen inter-regional cooperation and coordination; and lastly, differentiated low-carbon policies should be formulated to address the characteristics of aging in different regions and stages in order to promote the synergistic development of a low-carbon society and an aging society.

Analyzing the spatiotemporal differences in land use carbon emissions systematically and exploring their influencing factors for the rational allocation of land resources is of great importance and promoting collaborative emission reduction in this region. Based on the calculation of land use carbon emissions in Ningxia and its prefecture-level cities from 2000 to 2021, the regional differences in carbon emissions, economic efficiency, and carbon sink capacity were reflected through the difference index, carbon emission intensity, economic contribution rate, and carbon sink ecological carrying capacity. The results were as follows： ① From 2000 to 2021, the land use carbon emissions in Ningxia showed a significant increase by 110 919 400 t. Construction land was the main carbon source land, accounting for 99.57% of the total carbon emissions in 2021, and forest land was the main type of carbon absorption, accounting for 79.22% of the total carbon absorption in 2021. ② During the research period, the carbon emission difference among prefecture-level cities showed a trend of first rising and then slightly falling, with the gap reaching the maximum in 2016. ③ Although the overall difference in carbon emission intensity among prefecture-level cities showed a trend of narrowing and convergence, the economic contribution coefficient and carbon sink ecological carrying coefficient had significant differences, and the economic contribution rate and carbon emission contribution rate were both in a relatively unbalanced state, with obvious regional differences. ④ Land use carbon emission intensity, land use structure, economic development level, and population all played a promoting role in land use carbon emission, with contribution rates of 56.48%, 41.27%, 85.20%, and 9.29%, respectively. The contribution value of land use carbon intensity per unit GDP was negative, which inhibited the increase of land use carbon emission.

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

The most significant cost of increase in economic growth is an increase in energy consumption and carbon emissions as well. Energy consumption triggers carbon dioxide emissions, which is the main cause of environmental pollution. In recent years, struggling with climate changes, global warming and carbon dioxide emissions based environmental problems became critical issues. In doing so, this study investigates the relationship between carbon emissions and economic growth for BRICS-T countries for the period of 1992-2016. We apply Pedroni and, Westerlund and Edgerton panel cointegration approaches for examining cointegration between the variabes. The FMOLS approach is applied for testing long-term relationship between economic growth and carbon emissions. The empirical results indicate that a 1% increase in economic growth increases carbon emissions by 0.79% but 1% increase in carbon emissions leads economic growth by 0.5%. The causality analysis reveals the presence of bidirectional relationship between carbon emissions and economic growth.

The investigation of the spatiotemporal trajectory and driving factors of urban carbon emissions is crucial for achieving carbon peaking， controlling global carbon emissions， and protecting the ecological environment. Compared with the traditional carbon emission-driven identification， the Bayesian hierarchical spatio-temporal model can deal with more complex nonlinear and multilateral variable relationships， better cope with data missing， and improve the estimation accuracy of the results. Based on this， this study uses the carbon accounting coefficient method to measure the carbon emissions of urban agglomerations in the middle reaches of the Yangtze River. The Theil index， gravity center migration model， and spatial autocorrelation analysis are used to explore the spatial and temporal trajectory of urban carbon emissions. Further， the Bayesian hierarchical spatial-temporal model is used to analyze the driving factors affecting carbon emissions. The results showed that： ① The total carbon emissions of the study area increased from 78 459.68×104 t in 2006 to 123 350.56×104 t in 2021， with the rate of increase in carbon emissions slowing down from 1.95% to 1.61%. The Wuhan urban agglomeration was the core area for carbon emissions， accounting for 47.48% of the total emissions. The difference in carbon emissions in the Poyang Lake urban agglomeration was the largest； the focus of carbon emissions changed from south to north. ② The carbon emissions of the urban agglomeration in the middle reaches of the Yangtze River exhibited significant spatial correlation， primarily manifesting as high-high or low-low clustering types， and displayed a spatial distribution characteristic of higher emissions in the west and lower emissions in the east. ③ The degree of influence of driving factors on carbon emissions was ranked as follows： urbanization rate>industrial structure>level of economic development>actual use of foreign capital>energy efficiency>expenditure on science and technology>total population. The positive impact of urbanization rate， economic development level， actual utilization of foreign capital， and energy efficiency on carbon emissions was gradually increasing， the positive impact of industrial structure on carbon emissions was gradually weakening， and the positive impact of science and technology expenditure and total population on carbon emissions was fluctuating. The local changes in carbon emissions in the study area were obviously different， and the overall performance was 'weak in the upper part and strong in the lower part'. The hot spots were mainly concentrated below the study area， the local trend of carbon emissions in the study area was obviously different， and the rapid growth area was mainly distributed in Wuhan urban agglomeration. The study results are significant for understanding the spatiotemporal characteristics of carbon emissions and their driving variables and hold important theoretical and practical implications for the subsequent application of Bayesian hierarchical spatiotemporal models in the field of carbon emissions.

Agricultural carbon emissions are crucial for achieving carbon peaking and carbon neutrality in China.Based on the emission factor method, ecological pressure coefficient and grey correlation model, the temporal and spatial evolution of agricultural carbon emissions in Anhui province and their influencing factors are explored.The following results are obtained: From 2001 to 2022, the agricultural carbon emissions in Anhui province showed a trend of decreasing -increasing -declining-increasing, especially from 2001 to 2007, with an average annual decline of 4.58%.The agricultural carbon emission intensity showed a trend of fluctuating decline; the agricultural carbon absorption was also increasing year by year.The total agricultural carbon emissions in Anhui province basically decreased from north to south, and the carbon emission intensity of traditional agricultural areas was higher than that of areas with good industrial base.The spatial distribution of ecological pressure coefficients of carbon emissions was basically consistent with the distribution trend of total carbon emissions, and the pressure coefficients were between 0.7 and 1.8.Grey correlation analysis showed that the core affecting factors of agricultural carbon emissions were agricultural industrial structure, agricultural production scale, scientific and technological factors, economic development and agricultural employees.