Regional difference decomposition and its spatiotemporal dynamic evolution of Chinese agricultural carbon emission: considering carbon sink effect

Re-measurement of agricultural carbon emission rates: Drivers, regional differences and dynamic evolution

This research paper introduces a novel approach by combining a Backpropagation (BP) neural network with a non-angular and non-radial directional distance function to construct a BPNN-DDF model. This innovative model evaluates, decomposes, and analyzes China's agricultural sector's carbon emission rate across nine key subregions between 2010 and 2021. The key findings of this study are that China's agricultural carbon emission rate is decreasing, primarily due to technological advancements rather than technological efficiency. Subregions with robust economies and stable climates exhibit higher carbon emission efficiency, whereas those with underdeveloped economies, low agricultural technology, and volatile climates show relatively lower efficiency. The Dagum Gini coefficient analysis reveals a widening disparity in carbon emission rates among agricultural subregions, escalating from 0.174 in 2010 to 0.425 in 2021, indicating a growing gap between subregions that demands immediate attention. The kernel density distribution demonstrates an overall upward trend in China's carbon emission efficiency but also highlights an increasing divergence among subregions, particularly between the South China Area, the Huang-Huai-Hai Plain, and other regions. Therefore, this paper posits that strategies focusing on technological progress, sustainable agricultural development, regional development initiatives, and addressing inter-subregional imbalances will be crucial pathways for China's future low-carbon agricultural development.

Spatio-temporal pattern and the evolution of the distributional dynamics of county-level agricultural economic resilience in China

Because the complexity of the external environment has put great pressure on the agricultural economy, making it vulnerable, it is necessary to promote a system of resilience in the agricultural economy so that Chinese agriculture can continue to persevere in the face of serious external uncertainties. Therefore, this paper investigates the spatio-temporal pattern and evolution of the distributional dynamics of China's county-level agricultural economic resilience based on 2000-2020 data covering 2,545 counties. The results are as follows: first, from 2000 to 2020, the mean value of China's county-level agricultural economic resilience showed an obvious upward trend, which indicates that China's agricultural economy gradually increased its ability to resist risks and continued to develop in a favourable manner. Specifically, the county-level agricultural economic resilience index of the northeast region grew the most significantly, while the index of county units in the western region was relatively low. Second, the centre of gravity of the spatial distribution of China's agricultural economic resilience gradually migrated to the northwest, showing a dominant direction from northeast to southwest and a tendency to develop from southeast to northwest. Third, the spatial differences in China's agricultural economic resilience generally showed an upward trend, while county-level differences were the main source of the overall differences, followed by inter-provincial differences, inter-municipal differences and inter-regional differences. Additionally, the contribution of county-level differences to the overall differences fluctuated within the range of 54%-58%. Fourth, there is a possibility of localized convergence in China's agricultural economic resilience, which is continuous in spatial effects and has obvious positively correlated spatial effects at different times and in different county spaces.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Spatiotemporal differentiation and convergence characteristics of green economic efficiency in China: from the perspective of pollution and carbon emission reduction

Accurate quantification of pollution and carbon emission reduction policies, as well as analysis of green economic efficiency (GEE), are of great significance to accelerating green economic development in China and contributing to pollution prevention and carbon peaking. Using data from 2006 to 2022, this study incorporates pollution and carbon emission reduction policies into the evaluation system, and uses a model with slacks-based measures and a directional distance function (SBM-DDF) to calculate the GEE of 30 provinces. The Dagum Gini coefficient, kernel density estimation, and spatiotemporal convergence analysis are used to analyze the spatiotemporal differentiation and convergence characteristics of GEE. The findings show that the strengths of the pollution and carbon emission reduction policies are increasing but vary greatly among the provinces. China's overall GEE has a time trend with the characteristics of "decline-fluctuation-stable." The Dagum Gini coefficient reveals the relative differences between the major regions. Both the intra-regional and inter-regional differences tend to widen over time and the latter explains most of the sources of the overall differences. Kernel density estimation shows that the absolute differences between the provinces are generally widening, whereas the absolute differences between the provinces in the central and western regions are smaller than those in the eastern region. No obvious σ convergence characteristics exist in the country overall and the three major regions, but β convergence characteristics are present in each region. The factors affecting changes in the GEE of each region are not the same. The study suggests that the China should further improve the implementation of pollution and carbon emission reduction policies, pay attention to the regional differences and convergence issues of GEE, and promote the coordinated development of green economy in different regions. This study innovatively quantifies the policies related to pollution and carbon emission reduction, providing empirical evidence for understanding the performance of pollution and carbon emission reduction policies in various regions. Furthermore, this study incorporates policies as inputs into the GEE evaluation system, reveals the spatiotemporal differentiation of GEE, thereby providing reference for green economic transformation and sustainable development.

The study on the characteristics of carbon pressure agglomeration and the dynamic evolution of heterogeneity in China from a regional perspective

Provinces (cities and districts) with identifiable boundaries are under intense pressure to reduce emissions as a fundamental unit and research object of carbon peaking and carbon-neutrality goals. Due to the significant variability of regional development, achieving equilibrium between carbon emissions and carbon absorption is challenging, contributing to the difficulty of developing carbon emission reduction and relevant green strategic initiatives in China. Therefore, this paper explored the spatial effect of carbon balance with carbon pressure as the starting point. First, this paper defined the "carbon pressure index" (CPI) of 30 provinces (cities and districts) in China from 2000 to 2019. Second, this paper validated the CPI agglomeration evolutionary characteristics in global and local aspects based on the Moran's index. Third, this paper identified and decomposed the spatial heterogeneity of CPI using the kernel density estimation method and the Theil index, then extracted typical cities to analyze the specific causes. Finally, this paper classified the seven regions in China into four types according to a comprehensive analysis of CPI. The results indicated that China's ecological carbon cycle system was in a serious "carbon overload" state. Thirty provinces (cities and districts) showed significant spatial agglomeration characteristics. The spatial gap of CPI was gradually decreasing nationwide, and the intra-regional differences were the leading cause of CPI levels in China. This can provide policy basis for the improvement of China's balanced development system of regional carbon emission reduction.

Analysis of regional differences and spatial spillover effects of agricultural carbon emissions in China

In order to realize "double carbon" target in agriculture and high-quality development of the rural economy in China, it is crucial to study the regional differences and spatial spillover effects of agricultural carbon emissions (ACE). This paper measures ACE using panel data of 31 Chinese provinces from 2005 to 2020, examines the spatio-temporal evolution characteristics，the convergence of agricultural carbon emissions, compares and analyzes regional differences, and investigates the spatial correlation and spatial spillover effects. The study found that: (1) Total agricultural carbon emissions over the research period exhibit a rising and then reducing trend, the spatial distribution of total agricultural carbon emissions is described as high in east-central and low in west. The gap of agricultural carbon emissions is gradually declining in the east, and will eventually reach their respective steady-state levels in the west and northeast. (2) There is a strong spatial interprovincial link of ACE, which has a beneficial knock-on effect on the convergence of adjacent provinces. (3) Agricultural industrial structure, urbanization level, the size of the agricultural labor force, and the intensity of the agricultural machinery input all directly affect ACE in this province and indirectly affect ACE in adjacent provinces, with the exception of the negligible coefficient of economic development level on ACE. Hence, pertinent policy suggestions are put out to serve as a guide for reducing ACE.

Spatiotemporal characteristics and determinants of agricultural carbon offset rate in China based on the geographic detector

This paper attempts to explore the spatiotemporal variation characteristics of the agricultural carbon offset rate (ACOR) and the reasons that shape its differentiation characteristics in China. To achieve this objective, the Dagum Gini coefficient, kernel density estimation, and geographic detector model are employed in this study. The results show that there are some differences in ACOR among regions in China. Interregional differences are the main source of their overall variation. Excluding the spatial conditions, the ACOR of each province in the sample period shows low mobility characteristics. Considering the spatial conditions, there is convergence in the lower-middle neighborhoods. The three-year lag period did not significantly affect the interaction of ACOR between regions under the accession time horizon. At the aggregate level, the spatial and temporal divergence in China's ACOR is driven by urbanization rate, agricultural fiscal expenditure, and rural education level. As for the regional level, the scale of household farmland operation plays a greater role in determining the spatiotemporal variation of the eastern and central regions' ACOR. While urbanization rate is more determinant for the western region, the interaction between any two factors has significantly higher explanatory power for the spatial and temporal variation of ACOR than the single factor.

The influences of the advancement of green technology on agricultural CO2 release reduction: A case of Chinese agricultural industry

The development of green technology (GT) may have a vital influence in decreasing carbon releases, and the linkage between the advancement of GT and CO2 releases in China's agricultural industry has not attracted enough attention. The main objectives of this study are to assess the influence of agricultural green technology advancement on efficiency enhancement, release control capabilities, agricultural energy structure, and agriculture industrial structure. This article decomposes the advancement of green technology (AGTP) in the agricultural industry in China into resource-saving green technology advancement (AEGTP) and emission reduction green technology advancement (ACGTP). At the same time, to evaluate the intermediary impact of green technology advancement, a two-step econometric model and an intermediary impact model were utilized to evaluate the panel data of 30 provinces in China from 1998 to 2018. The role of AGTP (including ACGTP and AEGTP) and CO2 release concentration has also been explored critically. The results show that (i) under the two-step measurement method, AGTP has substantial favorable impacts on agricultural energy efficiency (EF) and possesses a negative impact on agriculture industrial structure (PS) and agricultural energy structure (ES). Agricultural energy efficiency (EF) and agriculture industrial structure (PS) under AGTP will reduce CO2 release concentration, but the path of agricultural energy structure (ES) will increase CO2 release concentration. (ii) At the national level, AGTP has an immediate unfavorable influence on CO2 releases. After introducing the intermediary variables, the intermediary impact of AGTP on CO2 releases through agricultural energy efficiency (EF), agriculture industrial structure (PS), and agricultural energy structure (ES) is also significantly negative, and the direct impacts of each variable are higher than the intermediary impact. (iii) In terms of different zones, the direct impacts of AGTP are all significant. The order of significance of the direct impacts of different zones is west to central and central to eastern. The overall significance ranking of the mediating impact is ACGTP > AEGTP > AGTP, and the significance ranking of each index is ES > EF > PS. Finally, this article puts forward some policy recommendations to reduce CO2 releases.

North-south regional differential decomposition and spatiotemporal dynamic evolution of China's industrial green total factor productivity

"Green development" has become the way for countries around the world to strengthen industries, and it is an important part of China's high-quality economic development. The key for China to strike a balance between economic growth and environmental management is to optimize green total factor productivity (GTFP). This paper measures the GTFP of industry in 30 provinces of China from 2003 to 2019, based on the perspective of energy and carbon emission constraints. It empirically examines the spatial disequilibrium and dynamic evolution of industrial GTFP in China using Dagum Gini coefficients, Kernel density estimation, and Markov chain analysis. The study finds that, (1) although China's industrial GTFP is not high, it shows an increasing trend. The industrial GTFP in the southern region is higher than that in the northern region. (2) Technical efficiency is the shortcoming of China's industrial GTFP improvement. Technological progress is the main driving force of China's industrial GTFP improvement. (3) The relative and absolute differences in China'' industrial GTFP, technical efficiency, and technological progress have all shown a widening trend. Regional differences between the southern and northern regions are the main source of relative differences in industrial GTFP, technical efficiency, and technological progress. (4) China's industrial GTFP shows a clear "club convergence" phenomenon and the "Matthew effect." However, after the introduction of the spatial factor, the "club convergence" phenomenon and the "Matthew effect" have weakened. The driving effect of industrial GTFP on neighboring provinces is stronger in the south than in the north. This paper enriches the analysis of industrial GTFP and provides an important basis for the coordinated regional development of Chinese industry.

Exploring the impact of rural labor transfer on the production and ecological sustainability of crop planting structure in China

With the improvement of industrialization, numerous rural laborers migrate to urban areas in search of off-farm jobs. Farmers change agricultural production decisions to adapt to the change of labor force, which will inevitably affect the crop planting structure. However, few studies have explored the sustainability of crop planting structure. Based on the calculation of the multiple cropping index (MCI), grain crops planting rate (GCR), economic crops planting rate (ECR), and ecological sustainability index (ESI) of crop planting structure, this study analyzes the impact of labor transfer rate (LTR) and labor cost (LC) on the sustainability of crop planting structure using a geographically and temporally weighted regression (GTWR) model. The results show that the scale of rural labor transfer and labor cost in China remains on the rise, but the growth rate has slowed down. The total carbon absorption of crops in China shows a U-shape trend, and the rice and maize have the largest carbon absorption. The impact of LTR on MCI is mainly positive, especially in the North China Plain in the early stage and some provinces in the Southwest China in the later stage. The impact of LTR on ECR and ESI is negative in most provinces. And the negative influence of LC on MCI is increasing, showing the spatial distribution characteristics of large influence in the southeast and small influence in the northwest. The impact of LC on ESI shows a negative effect in most provinces in the early stage, and the negative effect is more concentrated in some provinces in the southwest in the later stage.

Regional differences and convergence of green innovation efficiency in China

Green innovation facilitates high-quality economic development and ecological environmental protection. Herein, the minimum distance to strong efficient frontier (MinDS) model was used to measure green innovation efficiencies (GIEs) of 30 Chinese provinces over a period of 21 years (2000-2020). Gini coefficient decomposition and kernel density estimation methods were used to analyze the regional differences of GIE. Spatial correlation was estimated to analyze spatial-spillover effects and spatial convergence of the GIE. China's GIE has shown an increasing trend with significant spatial differences in GIE among provinces. Regional differences and transvariation intensity are the primary sources of spatial differences in GIE. Regional differences in GIE have decreased, except for eastern regions. The results of spatial convergence estimation suggest spatial absolute and conditional convergence in all regions. Therefore, for the GIE improvement in China, the effects of economic level, industrial structure, and environmental regulations must be considered.

Study on the Spatiotemporal Evolution and Influencing Factors of Agricultural Carbon Emissions in the Counties of Zhejiang Province

The accurate measurement of agricultural carbon emissions and the analysis of the key influential factors and spatial effects are the premise of the rational formulation of agricultural emission reduction policies and the promotion of the regional coordinated governance of reductions in agricultural carbon emissions. In this paper, a spatial autocorrelation model and spatial Dubin model are used to explore the spatiotemporal characteristics, influential factors and spatial effects of agricultural carbon emissions (ACEs). The results show that (1) From 2014 to 2019, the overall carbon emissions of Zhejiang Province showed a downward trend, while the agricultural carbon emission density showed an upward trend. ACEs are mainly caused by rice planting and land management, accounting for 59.08% and 26.17% of the total agricultural carbon emissions, respectively. (2) The ACEs in Zhejiang Province have an obvious spatial autocorrelation. The spatial clustering characteristics of the ACEs are enhanced, and the "H-H" cluster is mainly concentrated in the northeast of Zhejiang, while the "L-L" cluster is concentrated in the southwest. (3) The results of the Dubin model analysis across the whole sample area show that the ACEs exhibit a significant spatial spillover effect. The disposable income per capita in the rural areas of the county significantly promotes the increase in the ACEs in the neighboring counties, and the adjustment of the industrial structure of the county has a positive effect on the agricultural carbon emission reductions in neighboring counties. (4) The grouping results show that there is heterogeneity between 26 counties in the mountainous areas and non-mountainous areas. In the 26 mountainous counties, the urbanization rate, rural population, mechanization level and industrial structure have significant negative spatial spillover effects on the carbon emissions. In the non-mountainous counties, the agricultural economic development level and disposable income per capita of the rural residents have significant spatial spillover effects on the agricultural carbon emissions. These research results can provide a theoretical basis for the promotion of the development of low-carbon agriculture in Zhejiang according to the region and category.

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, 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.

Exploring the spatiotemporal heterogeneity and influencing factors of agricultural carbon footprint and carbon footprint intensity: Embodying carbon sink effect

Due to the combined effects of carbon emission and carbon sink, agriculture is acknowledged as an essential contributor to achieve the Chinese government's carbon neutrality goal of 2060, and carbon footprint (CF) and carbon footprint intensity are substantial indicators to reveal the carbon emission level. For these reasons, the Theil index technique and extended STIRPAT model were employed to evaluate their spatiotemporal heterogeneity and influencing factors using panel data from 31 provinces for the period 1997-2019. The findings revealed that the CF showed an increasing trend with an annual growth rate of 24.6 %. The carbon footprint intensity (CFI) indicated an evident spatiotemporal heterogeneity and transferred over time, with an average growth rate of 19.82 %. The CFI Theil index and its contribution rate both confirmed that intra-regional difference is the main source of the overall difference, among which, the CFI Theil index displayed the distribution feature of "western (11.50 %) > central (11.12 %) > eastern (10.56 %) > northeast (6.61 %). The contribution rate of CFI illustrated the spatial pattern of "eastern (33.74 %) > central (21.07 %) > western (19.87 %) > northeast (5.24 %). Furthermore, the influencing effects of GDP per capita, planting structure, population density and urbanization level on CF and CFI also demonstrate evident spatiotemporal heterogeneity.

Identifying the impact factors of sustainable development efficiency: integrating environmental degradation, population density, industrial structure, GDP per capita, urbanization, and technology

To accomplish the high-quality development target in Yellow River Basin, the current study investigates the impact factors of the rural sustainable development efficiency in Yellow River Basin from the period of 1997 to 2017, by using Super efficiency Slack-based Measure, improved STIRPAT, and the OLS regression. The findings illustrate that rural sustainable development efficiency in Yellow River Basin is maintaining a fluctuating upward trend during the investigation. The impact factor analysis reveals that at the entire basin level, the population density and industrial structure have the greatest impact on rural sustainable development efficiency, while the technology level has the least impact. The industrial structure and GDP per capita negatively impacted rural sustainable development efficiency in the upper and middle basin, while they have non-significant positive impact in the lower basin. Besides, urbanization level inhibited rural sustainable development efficiency in upper basin (except middle basin and lower basin), and technology level has promotional effect in rural sustainable development efficiency at the entire basin as well as at the 3 sub-basins, while the influence effect is not significant in the lower basin. Therefore, these empirical results indicate that the impact effect of these factors exist spatial heterogeneity. Thus, decision-makers should consider this reality fully and make differential measures when they construct the development long-term strategies for rural sustainable development efficiency in yellow river basin.

Digital Economy, Agricultural Technological Progress, and Agricultural Carbon Intensity: Evidence from China

China is the largest carbon emitter in the world, with agricultural carbon emissions accounting for 17% of China’s total carbon emissions. Agricultural carbon emission reduction has become the key to achieving the “Double Carbon” goal. At the same time, the role of the digital economy in achieving the “dual carbon” goal cannot be ignored as an important engine to boost the high-quality development of China’s economy. Therefore, this paper uses the panel data of 30 provinces in mainland China from 2011 to 2019 to construct a spatial Durbin model and a mediation effect model to explore the impact of the digital economy on agricultural carbon intensity and the mediating role of agricultural technological progress. The research results show that: (1) China’s agricultural carbon intensity fluctuated and declined during the study period, but the current agricultural carbon intensity is still at a high level; (2) The inhibitory effect of the digital economy on agricultural carbon intensity is achieved by promoting agricultural technological progress, and the intermediary role of agricultural technological progress has been verified; (3) The digital economy can significantly reduce the carbon intensity of agriculture, and this inhibition has a positive spatial spillover effect. According to the research conclusions, the government should speed up the development of internet technology and digital inclusive finance, support agricultural technology research and improve farmers’ human capital, and strengthen regional cooperation to release the contribution of digital economy space.

Research on carbon emission differences decomposition and spatial heterogeneity pattern of China's eight economic regions

To explore the sources of regional carbon emission differences and the evolution characteristics of spatial heterogeneity pattern, this paper first calculates the corresponding carbon emissions according to the relevant statistical data of eight economic regions in China from 2005 to 2019. It analyzes the overall differences and temporal and spatial evolution characteristics of regional carbon emissions combined with the visualization method of GIS. Then, the total carbon emission difference is decomposed by the Theil index to find out the primary sources affecting the regional carbon emission difference. Finally, the driving factors affecting the spatial heterogeneity pattern of regional carbon emissions are studied with the help of the Geodetector method. The results show that (1) significant differences in carbon emissions among China's eight economic regions. The contribution rate of inter-regional and intra-regional differences of carbon emissions in different regions to the overall carbon emission difference is diverse. (2) Regional carbon emissions are affected by single driving factors and the interaction of two driving factors. The interaction has an increasing impact on the determinant of regional carbon emission spatial differentiation. (3) The factor detection results and interaction detection results, respectively, show that the level of energy consumption, industrialization, and technological development has always been the main driving factors affecting the spatial heterogeneity pattern of regional carbon emissions, and the critical interaction factors have multiple spatial superposition interaction effects. Therefore, regional carbon emission reduction should consider the national strategic objectives and own regional characteristics and implement differentiated emission reduction schemes.

Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China

The rapid development of public buildings has greatly increased the country’s energy consumption and carbon emissions. Excessive carbon emissions contribute to global warming. This paper aims to measure the carbon emissions in the operation of public buildings, and to identify the multiple influencing factors of carbon emissions in operational public buildings. First, the spatial and temporal variation characteristics of carbon emissions from public buildings in 30 provinces of China from 2008–2019 are analyzed. Second, a green building index is constructed, and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to explore the relationship between each influencing factor and carbon emissions, using spatial and temporal geographically weighted regression analysis. The results show that the effects of population, urbanization rate, GDP per capita, green building index, and industrial structure on carbon emissions from public buildings all show spatial correlation and differences. There are east-west differences in the operational carbon emissions of public buildings in China’s provinces. Cluster evolution shows a spatially increasing trend from west to east. To some extent, policymakers can develop appropriate policies for different provinces through the findings.