Coupling and decoupling effects of agricultural carbon emissions in China and their driving factors

Space-time cube uncovers spatiotemporal patterns of basin ecological quality and their relationship with water eutrophication

Maintaining an optimal eco-environment is important for sustainable regional development. However, existing methods are inadequate for examining both spatial and temporal dimensions. Here, we propose a systematic procedure for spatiotemporal examination of the eco-environment using the space-time cube (STC) model and describe a preliminary investigation of the coupling relationships between basin ecological quality and water eutrophication in upstream of the Han River basin between 2000 and 2020. The STC model considers the temporal dimension as the third dimension in calculations. We first categorized the basin into three sub-watershed types: forest, cultivated land, and artificial surface. Subsequently, the ecological quality and driving factors were assessed and identified using the remote sensing ecological index (RSEI) and Geodetector method, respectively. The findings indicated that the forest basin and artificial surface basin had the highest and lowest ecological quality, respectively. The spatiotemporal cold spots of ecological quality during the past 20 years were mostly located in the vicinity of reservoirs, rivers, and artificial surface areas. Human activity, precipitation, and the percentage of cultivated land were other important driving factors in the artificial surface, forest, and cultivated land sub-watersheds, respectively, in addition to the dominant factors of elevation and temperature. The results also indicated that when the ecological quality degraded to a certain extent, water eutrophication was significantly coupled with the ecological quality of the catchments. The findings of this study are useful for ecological restoration and sustainable river basin development.

Innovative measurement, trade-off-synergy relationship and influencing factors for agricultural net carbon emissions and effective supply of agricultural products in China

Sensitive zone of global climate change has been formed in China, and it has become a hot topic how can agriculture ensure food security and the supply of important agricultural products while achieving the "Dual Carbon" goal in the country. Based on such background, this paper uses the IPCC carbon emission calculation method, environmental input-output model and economic-water-carbon coefficient method to measure agricultural net carbon emissions, adopts bivariate spatial auto-correlation analysis and SYS-GMM to explore separately the relationship between agricultural net carbon emissions and effective supply of agricultural products, as well as the carbon reduction effect, growth effect and reasonable range of green technology innovation. The results show that: (1) China's agricultural net carbon emissions reveal a spatial distribution of "higher in the east than in the west than in the center" and a temporal characteristic of increasing year by year; China's effective supply of agricultural products shows an increasing trend and a spatial distribution of "higher in the east than in the center than in the west" in 2006-2012 and "higher in the east than in the west than in the center" in 2013-2020. (2) In 2006, 2010, 2015 and 2020, the number of provinces that belong to low-low agglomeration trade-off zone, low-high agglomeration synergy zone, non-significant zone, high-low agglomeration non-trade-off-synergy zone and high-high agglomeration trade-off zone averagely accounted for 12.500 %, 30.000 %, 26.667 %, 9.167 % and 21.667 % of the totality, respectively. (3) The carbon reduction and production growth effects of green technology innovation both show an inverted "U-shape", and green technology innovation is conducive to both reducing agricultural net carbon emissions and improving supply of agricultural products when it is within a reasonable range of greater than 0.930. (4) Green technology innovation not only has significant spatial and temporal heterogeneity impact, but also exhibits a differential effect on productive agricultural carbon emissions, agricultural trade carbon emissions, agricultural carbon sinks, total output of agricultural products and agricultural net imports in international trade. Therefore, it is proposed that China should establish and improve green technology innovation incubation platforms, guide all participants to ensure the investment and application of green technology products within a reasonable range, formulate and implement regional differential policies and plan in accordance with local conditions, drive ultimately coordinated promotion of agricultural carbon emission reduction and product supply guarantee and lay an important foundation for achieving high-quality economic development and efficient ecological protection.

The effect of environmental regulation and green subsidies on agricultural low-carbon production behavior: A survey of new agricultural management entities in Guangdong Province

Agricultural low-carbon production emerges as a pivotal function for achieving sustainable green development. However, there remains insufficient empirical evidence regarding the effect of environmental regulations and green subsidies upon the low-carbon production behavior of new agricultural entities. In this study, a questionnaire survey was administered to 268 respondents representing new agricultural entities in Guangdong Province, P.R.C. Subsequently, a structural equation model had been employed for validation analysis. This study's findings demonstrate that in general, environmental regulations positively and significantly affect the behavior of agricultural low-carbon production. Conversely, the influence of green subsidies is not statistically significant. In addition, differences are observed across different sectors, with environmental regulations significantly affecting low-carbon production behavior in the plantation sector, but not in the livestock sector. Conversely, green subsidies significantly impact low-carbon production behavior in the livestock sector, but not in the plantation sector. These findings highlight the promotional role of government-enforced environmental regulations and green subsidies in fostering low-carbon agricultural practices. Therefore, new agricultural entities should strive to augment green production technology capacities to realize sustainable green development.

Agricultural carbon emissions in Zhejiang Province, China (2001-2020): changing trends, influencing factors, and has it achieved synergy with food security and economic development?

Given the huge carbon footprint of agricultural activities, reduction in agricultural carbon emission (ACE) is important to achieve China's carbon peaking and carbon neutrality goals, but it may affect agricultural food security and economic development. Therefore, it is important for scientific carbon reduction measures to understand the multi-year trends and the influencing factors of ACE, and clarify whether the process of ACE affects food security and economic development. This study analyzed the trends of total ACE and ACE caused by different agricultural carbon sources (ACS) from 2001 to 2020 in Zhejiang Province, then we revealed the main influencing factors of ACE based on the logarithmic mean Divisia index (LMDI) model and dissected the relationship between ACE and food security and economic development. Results show that the total ACE fluctuated from 6.10 Mt in 2001 to 3.93 Mt in 2020, and the process included a decrease in 2001-2003 and 2005-2020 and an increase in 2003-2005. The decrease in ACE, from 2001 to 2014, was mainly due to the decline in rice acreage, which contributed 90.38%; from 2014 to 2020, it was by the reduction in the use of fertilizer, diesel, and pesticide, which contributed 83.9%. As drivers, agricultural economic development effect and total population size effect drove 4.25 and 1.54 Mt of ACE, respectively. As inhibitors, planting structure effect, technology development effect, and population structure effect inhibited 3.12, 2.11, and 2.74 Mt of ACE, respectively. With the reduction of ACE, the agricultural economy continued to grow, but the food security situation was pessimistic, indicating that ACE reduction has achieved synergy with economic development, but not with food security.

New evidence on the impact of No-tillage management on agricultural carbon emissions

Controlling agricultural carbon emissions contributes to achieving peak carbon emissions and carbon neutrality. However, as a conservation management practice of farmland, the impact of No-tillage management (NTM) on agricultural carbon emissions needs to be further discussed. The main purpose of this paper is to assess the direct effect and spatial spillover effect of NTM on agricultural carbon emissions, revealing the regulating mechanism of NTM on agricultural carbon emissions and the combined application of NTM. Results indicate that NTM reduces agricultural carbon emissions, which is significant in the central and western regions, along with the primary grain, corn, and rice production areas, as well as the northern regions of the Huai River. Furthermore, the spatial spillover analysis reveals that the implementation of NTM increases agricultural carbon emissions in neighboring regions, but financial support and cross-regional services can negatively regulate the relationship between NTM and space agricultural carbon emissions. This paper also finds that combining straw-returning technology and NTM reduces agricultural carbon emissions. Building a cross-regional coordination mechanism, an incentive mechanism, and innovating the conservation tillage model is essential for promoting the NTM and achieving agricultural carbon reduction.

Decoupling relationship between greenhouse gas emissions from cropland utilization and crop yield in China: implications for green agricultural development

Developing low-carbon utilization of cropland is critical to coordinate agricultural production and environmental protection. Based on a theoretical analysis of greenhouse gas (GHG) emissions and crop production, this study examined the GHG emissions from cropland utilization in China and the decoupling process from crop yields with consideration of different sources and then explored the driving factors in different regions. The results showed that the GHG emissions from cropland utilization in China rose first and then fell between 2003 and 2020, and the decoupling process has undergone three stages, namely "expansive coupling", "weak decoupling", and "strong decoupling". And the eastern and southern provinces are relatively ahead of the western and northwestern provinces. Additionally, crop yields have been basically decoupled from GHG emissions caused by agricultural inputs, but they were still not decoupled from GHG emissions from cropland in Northeastern and Northern China. Among the influencing factors, utilization efficiency has promoted the decoupling progress, the input structure has played a promoting role in the early stage and hindered it later, and the input intensity and the scale have worked as hindering factors. Policy implications have been proposed to support the sustainable development of agriculture.

The nexus between indicators for sustainable transportation: a systematic literature review

The relationship between indicators for sustainable transportation is a pressing issue that has argued the attention of policymakers, engineers, and academics. The transportation sector plays a crucial role in economic growth, while also having significant environmental consequences. This systematic literature review offers a comprehensive overview of the different research methodologies utilized to estimate the interrelationships between the transport sector, environmental degradation, and economic growth. Our study analyzed 977 citations sourced from Web of Science and SCOPUS, spanning the years 2010 to June 2022. The PRISMA methodology was employed for organizing and identifying articles. After a thorough evaluation, 52 published articles from 25 international journals were selected for further examination. Our findings show that researchers have used a variety of modeling approaches to shed light on this complex issue, with multivariate co-integration techniques, decomposition analysis, and the generalized method of moments being among the most widely used methods in recent years. This review provides perspectives to policymakers and decision-makers, enabling them to create effective energy and environmental strategies for a long-term, sustainable transportation future.

Reducing the carbon emission from agricultural production in China: do land transfer and urbanization matter?

Urbanization and land transfer have triggered a profound reform of the Chinese agricultural sector since reform and opening, leading to a continuous rise in agricultural carbon emissions. However, the impact of urbanization and land transfer on agricultural carbon emissions is not widely understood. Therefore, based on the panel data covering 30 provinces (cities) in China from 2005 to 2019, we adopted a panel autoregressive distributed lag model and a vector autoregressive model to empirically explore the causal relationship between land transfer, urbanization, and agricultural carbon emissions. The main conclusions are as follows: (1) Land transfer can significantly reduce carbon emissions from agricultural production in the long run, while urbanization has a positive effect on agricultural carbon emissions. (2) In the short run, land transfer has a significant positive impact on agricultural carbon emissions, and urbanization also has a positive impact on the carbon emissions of agricultural production, but in insignificant ways. (3) There is two-way causality between land transfer and agricultural carbon emission, and between urbanization and land transfer is the same, but urbanization is the one-way Granger cause of agricultural carbon emissions. Finally, some suggestions are provided for low-carbon agriculture development: the government should encourage the transfer of land management rights and guide high-quality resources to gather in green agriculture.

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.

Spatio-Temporal Difference in Agricultural Eco-Efficiency and Its Influencing Factors Based on the SBM-Tobit Models in the Yangtze River Delta, China

In the Yangtze River Delta region, where the agricultural economy is well developed and agricultural pollution and carbon emissions are significant, a regional study of AEE (Agricultural Eco-Efficiency) is crucial to reducing agricultural environmental pollution, improving the rationalization of agricultural production layout, and promoting the realization of low-carbon goals. The SBM-Tobit model and GIS were employed to analyze AEE based on the carbon emission evaluation system in the spatial and temporal characteristics, as well as the influencing factors and the migration path of the center of gravity in the "low carbon" context. A rational plan of agricultural production was proposed according to the results. The following results were obtained: (1) The level of AEE in the Yangtze River Delta region was high, and the region exhibited a U-shaped curve change from 2000 to 2020, with a fluctuating decrease from 2000 to 2003 and a fluctuating increase from 2004 to 2020. The regional spatial development balance was enhanced, while there was a spatial incongruity in the development process of AEE enhancement, high in the southwest and low in the northeast; (2) AEE generally had a high regionalized agglomeration of low-low in the southwest and high-high in the northeast. Nonetheless, temporal heterogeneity was observed in spatial correlation, and the correlation weakened with time variation; (3) Urbanization level, agricultural production structure, crop cultivation structure, and fertilizer application intensity were the main factors influencing AEE in the Yangtze River Delta region; (4) The center of gravity of AEE in the Yangtze River Delta region shifted to the southwest under the influence of "low-carbon" related policies. Therefore, the improvement of AEE in the Yangtze River Delta region should focus on inter-regional coordination and linkages, rational planning of production factors, and the formulation of measures under relevant carbon policies.

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.

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

Agricultural mechanization service (AMS) is a critical path to achieving agricultural green transformation with smallholders as the mainstay of agricultural production. Based on the panel data of 30 Chinese provinces from 2011 to 2020, this paper measures the AGTFP using the Super-SBM model and examines the effects of different AMS supply agents on AGTFP and spatial spillover effects through the spatial Durbin model. The main conclusions are as follows: First, China's AGTFP showed a stable growth trend, with the mean value increasing from 0.1990 in 2011 to 0.5590 in 2020. Second, the specialization (SPO) and large-scale (LSO) of AMS supply organizations have significantly positive effect on the AGTFP of the local province. However, SPO has a significantly positive effect on the AGTFP of the neighboring provinces, while LSO has the opposite effect. Third, the specialization of AMS supply individuals (SPI) has significantly negative effect on the AGTFP of the local province. In contrast, the large-scale AMS supply individuals (LSI) has the opposite effect. Furthermore, the spatial spillover effects of both are insignificant. Fourth, the spatial spillover effect of AGTFP shows asymmetry among different regions and indicates that AMS resources flow from non-main grain production and economically developed regions to main grain production and less developed regions. These findings provide helpful policy references for constructing and improving the agricultural mechanization service system and realizing the agricultural green transformation in economies as the mainstay of agricultural production.

Analysing the Effect of Energy Intensity on Carbon Emission Reduction in Beijing

Beijing has experienced rapid economic development since the reforms and opening up. However, the traditional development model based on excessive energy consumption has posed great challenges to the ecological environment. To curb environmental degradation and achieve sustainable social development, Beijing has proposed to achieve carbon neutrality by 2050. As an important indicator of energy consumption, it is necessary to clarify how energy intensity (EI) affects carbon emissions (CE) to achieve carbon neutrality in Beijing by 2050. This study first decomposes the drivers of CE in Beijing from 2010 to 2020 using the logarithmic mean Divisia index (LMDI) method and comparatively analyses the impact of EI on CE. Then, the spatial Dubin model (SDM) is used to analyse the spatial spillover effect of EI on CE at the regional level. Finally, the macro moderating role of economic development in the effect of EI on CE is analysed. The results show that the effect of EI has been the main driver of CE reduction in Beijing. Among the industrial sectors, manufacturing and transportation have had the greatest success in reducing CE through EI reduction. At the regional level, there is a spatial spillover effect of EI on CE, and the effect of carbon reduction through the spillover effect of EI is greater than the direct effect of EI. Economic factors have an enhanced moderating effect on the process of EI affecting CE, and this moderating effect has threshold properties.

Spatial-temporal evolution and convergence analysis of agricultural green total factor productivity-evidence from the Yangtze River Delta Region of China

Measuring regional differences in agricultural green total factor productivity (AGTFP) provides a basis for policy guidance on agricultural green development in the Yangtze River Delta (YRD) region. By constructing a two-period Malmquist-Luenberger index under the carbon emission constraint, we measure the AGTFP of cities in the YRD region from 2001 to 2019. Furthermore, adopting the Moran index method and the hot spot analysis method, this paper analyzes the global spatial correlation and local spatial correlation of AGTFP in this region. Moreover, we investigate its spatial convergence. The results show that the AGTFP of 41 cities in the YRD region is on an increasing trend; the growth of AGTFP in the eastern cities is mainly driven by green technical efficiency, while this growth in the southern cities is mainly stimulated by green technical efficiency and green technological progress. We also find a significant spatial correlation between cities' AGTFP in the YRD region from 2001 to 2019, but with certain fluctuations, showing a U-shaped trend of "strong-weak-strong". In addition, absolute β convergence of the AGTFP exists in the YRD region, and this convergence speed is accelerated with the addition of spatial factors. This evidence provides support for implementing the regional integration development strategy and optimizing the regional agricultural spatial layout. Our findings offer implications for promoting the transfer of green agricultural technology to the southwest of the YRD region, strengthening the construction of agricultural economic belts and agricultural economic circles, and improving the efficiency of agricultural resource use.

The role of digital finance in reducing agricultural carbon emissions: evidence from China's provincial panel data

As a vast agricultural country that emits a high level of agricultural carbon, China faces significant pressure to reduce its agricultural emissions. In recent years, digital finance has become a crucial part of China's financial system and has reshaped China's mode of green finance. Based on the 2011 to 2020 panel data of 31 provinces in China, this study discusses the effect and mechanisms of digital finance on agricultural carbon emissions. A two-way fixed effect model, threshold effect model, mediating effect model, and moderating effect model have been adopted to investigate the nexus of digital finance and agricultural carbon emissions. The results show that: (1) digital finance can reduce agricultural carbon emissions, and this effect is nonlinear, with two thresholds. (2) A reduction of agricultural carbon emissions through digital finance can be realized via digital finance's impact on farmers' entrepreneurship and agricultural technology innovation. (3) Urbanization has a positive moderating effect on digital finance's agricultural carbon emissions reduction effect. Based on the above conclusions, specific recommendations are proposed with regard to digital finance reducing agricultural carbon emissions.

An investigation of the relationship among economic growth, agricultural expansion and chemical pollution in Iran through decoupling index analysis

Due to the significant role of agricultural chemicals in increasing agricultural production and ensuring food security, the excessive use of chemical fertilizers and pesticides has been intensified in Iran. These chemical inputs are important environmental pollutants that threaten human health. In the recent years, in agricultural sector, the balance between the growth of agricultural economy and the spread of pollution in Iran has been one of the major challenges. In this regard, the use of decoupling index to decouple the link between agricultural production and pollution caused by the consumption of chemical inputs, such as fertilizers and pesticides, has been emphasized; Therefore, in the present study, the decoupling index first is calculated in relation to the emission of pollution caused by the use of chemical inputs in the process of agricultural production during the period of 1991-2016 in Iran. Then, by reviewing the existing literature systematically, the factors affecting the decoupling index in the agricultural sector of Iran are evaluated using the autoregressive distributed lag (ARDL) model. The results showed that in the recent years, pollution indicators in relation to chemical inputs have not had ideal trends, and despite the further growth of agricultural production, the quality of the environment has experienced a declining trend. The results of the decoupling index related to the use of chemical pesticides and fertilizers in Iran show that during a period of 26-year, only 5 and 4 years of using these inputs have had a sustainable state compared to the production growth; besides, a strong negative decoupling state occurred as the most unsustainable state in relation to chemical fertilizer for 7 years. Moreover, among the factors affecting the decoupling index, the value-added variable of the agricultural sector has had the most positive effect on this index, and thus, in the long run, it increases the level of pollution in the agricultural sector. The variables of gross domestic product (GDP) per capita and the area under cereal cultivation in the agricultural sector would also increase the decoupling index. Accordingly, adopting effective strategies to improve resource efficiency, planning for the implementation of biotechnological methods, and doing investment for creating green infrastructure in the agricultural sector can be effective in the ideal decoupling of pollution and agricultural economy growth in Iran.

Driving factors and decoupling trend analysis between agricultural CO2 emissions and economic development in China based on LMDI and Tapio decoupling

Based on mathematical models, in-depth analysis about the interrelationship between agricultural CO₂ emission and economic development has increasingly become a hotly debated topic. By applying two mathematical models including logarithmic mean divisia index (LMDI) and Tapio decoupling, this work aims to study the driving factor and decoupling trend for Chinese agricultural CO₂ emission from 1996 to 2020. Firstly, the intergovernmental panel on climate change (IPCC) method is selected to estimate the agricultural CO₂ emission from 1996 to 2020, and the LMDI model is adopted to decompose the driving factors of agricultural CO₂ emission into four agricultural factors including economic development, carbon emission intensity, structure, and labor effect. Then, the Tapio decoupling model is applied to analyze the decoupling state and development trend between the development of agricultural economy and CO₂ emission. Finally, this paper puts forward some policies to formulate a feasible agricultural CO₂ emission reduction strategy. The main research conclusions are summarized as follows: 1) During the period from 1996 to 2020, China's agricultural CO₂ emission showed two stages, a rapid growth stage (1996-2015) and a rapid decline stage (2016-2020). 2) Agricultural economic development is the first driving factor for the increase of agricultural CO₂ emission, while agricultural labor factor and agricultural production efficiency factor play two key inhibitory roles. 3) From 1996 to 2020, on the whole, China's agricultural sector CO₂ emission and economic development showed a weak decoupling (WD) state. The decoupling states corresponding to each time period are strong negative decoupling (SND) (1996-2000), expansive negative decoupling (END) (2001-2005), WD (2006-2015) and strong decoupling (SD) (2016-2020), respectively.

Decomposition and decoupling analysis of carbon emissions from agricultural economic growth in China's Yangtze River economic belt

In this study, carbon emissions from agricultural energy consumption (CEAEC) are fully analyzed using data from the Yangtze River Economic Belt (YEB) between 2000 and 2017. First, generalized LMDI is adopted to decompose the drivers of CEAEC into five components. Then, the decoupling indicator and the decoupling effort indicator are constructed to quantify the decoupling degrees and examine the government's emission reduction efforts, respectively. The results show that (1) CEAEC in the YEB has shown a phased increase, reaching a peak at 1732.2510⁴t in 2012. Except for some decreases found in Shanghai, Chongqing, and Guizhou, it is shown that all provinces' CEAEC have risen to varying degrees. In contrast, the intensity of CEAEC in the YEB has been declining since 2005. (2) The economic output effect acts as the major contributor to the growth of CEAEC, followed by the population effect. In contrast, both the energy intensity effect and the energy structure effect are the primary reasons for reductions in CEAEC. The spatial difference in CEAEC in the YEB increased significantly from 2000 to 2017. (3) There was an alternating change from decoupling to coupling and then to negative decoupling from 2000 to 2017. Based on the conclusions mentioned above, it is proposed that the formulation of low-carbon agricultural development strategies should consider the structural adjustment of agricultural energy consumption and the advancements of agricultural technology.

Research on coupling coordination and influencing factors between Urban low-carbon economy efficiency and digital finance—Evidence from 100 cities in China’s Yangtze River economic belt

China is a large country with rapid economic expansion and high energy consumption, which implies that the country’s overall carbon emissions are enormous. It is vital to increase urban low-carbon economy efficiency (ULEE) to achieve sustainable development of China’s urban economy. Digital finance is a significant tool to boost ULEE by providing a convenient and effective funding channel for urban low-carbon economic transformation. Analyzing the coupled and coordinated relationship between ULEE and digital finance is of vital importance for the sustainable development of the urban economy. This paper selects panel data of 100 cities in China’s Yangtze River Economic Belt (YEB) in 2011-2019, and analyzes the research methods such as the Global Malmquist-Luenberger index model, coupling coordination degree (CCD) model, standard deviation ellipse model, gray model, and geographic detector by The spatial and temporal distribution, dynamic evolution characteristics and influencing factors of the CCD between ULEE and digital finance are analyzed. The study shows that: (1) the CCD of ULEE and digital finance grows by 3.42% annually, reflecting the increasingly coordinated development of the two systems; (2) The CCD of ULEE and digital finance shows a distribution pattern of gradient increase from the upstream region of Yangtze River to the downstream region, meanwhile, the spatial center of gravity moves mainly in the midstream region; (3) The spatial center of gravity of CCD of ULEE and digital finance is expected to move 22.17 km to the southwest from 2019 to 2040; (4) In terms of influencing factors, the influence of informatization and industrial structure on the CCD increases over time, while the influence of factors such as population development, greening, transportation, and scientific research decreases over time. Finally, this paper proposes policy recommendations for improving the CCD of ULEE and digital finance based on the empirical results.

The influence mechanism and spatial effect of carbon emission intensity in the agricultural sustainable supply: evidence from china's grain production

Agricultural carbon mitigation is critical for China to encourage the sustainable development of agriculture and achieve the carbon peak by 2030 and carbon neutrality by 2060. By exploring the impact mechanism of the carbon emission intensity (CEI) of grain production, we can effectively promote the low-carbon transformation of agricultural production and ensure the sustainable development of the food supply. This article analyzes the temporal and spatial evolution of the total carbon emission (TCE) and CEI of staple crops and adopts a dynamic spatial model to explore the influence mechanism and spatial spillover effects of the CEI of grain production based on evidence from China's major grain-producing provinces from 2002 to 2018. The results indicate that the TCEs of rice, wheat, and maize fluctuate upward and that the CEI in most producing areas decreases with low-low agglomeration (or high-high agglomeration). Among the influencing factors, technology is the main factor reducing CEI. Technical efficiency, urbanization, industrial structure, agricultural agglomeration, and agricultural trade openness can be transmitted to neighboring areas through spatial spillover mechanisms. The spatial spillover mechanisms are resource flow, technology spillover, and policy learning, producing the demonstration effect and siphon effect. Based on our findings, agricultural technology innovation and popularization, urbanization, optimization of the agricultural structure, financial payments, and factor flow among regions should be improved to encourage the low carbon transformation of grain production.

Decoupling Effect of County Carbon Emissions and Economic Growth in China: Empirical Evidence from Jiangsu Province

Under the pressure of low-carbon development at county level in China, this paper takes Jiangsu province as an example to analyze the relationship between economic growth and carbon emissions, aiming to provide a reference for the low-carbon development in Jiangsu and other regions in China. Based on the county-level panel data from 2000 to 2017, this paper uses the Tapio elasticity model and environmental Kuznets curve model, and focuses on the differences in regional economic development and the impacts of the 2008 global economic crisis. The results show that, in general, the decoupling effect of carbon emissions in Jiangsu counties has gradually increased during the study period. Since 2011, all counties achieved the speed decoupling, with more than half of them showing strong decoupling. The environmental Kuznets curves of carbon emissions in different income groups are established, and changed before and after the 2008 global economic crisis. In 2017, only 10 of the 53 counties were on the right side of the curve, realizing the quantity decoupling between the two. Therefore, to achieve a win–win situation between carbon emission reduction and economic growth, efforts should be made from the aspects of industrial structure and energy efficiency, and measures should be taken according to local conditions.

Short and long-run causal effects of agriculture, forestry, and other land use on greenhouse gas emissions: evidence from China using VECM approach

Climate change caused by different anthropogenic activities is a subject of attention globally. There is a concern on how to maintain a clean environment and at the same time achieve optimal use of land. To this end, this study examines the causal effects of land use including agricultural, forestry, and other land categories on greenhouse gas (GHG) emissions. The data for China is collected over the period 1990 to 2012 for the empirical examination. By employing vector error correction model (VECM), it is found that there is significant long-run causality among variables. However, in the short run expectedly, only land under agriculture has strong causality with the GHG emissions. The results in case of variance decomposition analysis highlight that land under agriculture and other use significantly causes the GHG emissions in the long run. Further, impulse responses of variables are also measured with the Cholesky one standard deviation. The results are robust and support the argument that different land uses cause GHG emissions in China. The study provides insights for policy makers to improve the activities occurring on agricultural and other land uses. Assessment of overall potential, including bio energy, needs to include analysis of trade-offs and feedbacks with land-use competition. Many positive linkages with sustainable development and with adaptation exist but are case and site specific as they depend on scale, scope, and pace of implementation.

What drives the decoupling between economic growth and energy-related CO2 emissions in China's agricultural sector?

Many studies have shown that the rapid agricultural mechanization development in China led to substantial energy consumption and CO₂ emission growth. To better explain the mechanism behind the decoupling between economic growth and CO₂ emissions, this paper extends the logarithmic mean Divisia index (LMDI) and production-theoretical decomposition (PDA) considering agricultural decoupling from both structural and technical perspectives. The results reveal that (1) China's agricultural decoupling performance was not ideal. Investment and investment efficiency were the most important factors influencing the decoupling status. The main decoupling obstacle was a higher investment in productivity rather than in energy conservation and carbon reduction. (2) The decoupling status and investment orientation of decoupling efforts among regions were different. Strong negative decoupling statuses frequently occurred in the eastern region, whose main disadvantage was high potential energy intensity. The decoupling status of the central region exhibited expansive features. The decoupling key is to invest more in energy-saving technology rather than in production. The western region changed from weak decoupling to expansive negative decoupling. Both output technology and energy-related factors should be the main investment targets. (3) Weak decoupling and expansive negative decoupling were the most common statuses among provinces. The influence mechanism of drivers exhibited a high spatial heterogeneity at the provincial level. Therefore, the study offered a convincing basis for local governments to formulate low-carbon agricultural development policies by identifying the main decoupling drivers.

Discerning drivers and future reduction paths of energy-related CO2 emissions in China: combining EKC with three-layer LMDI

Recognizing the process and identifying the drivers of energy-related CO₂ emissions can provide suggestions for designing carbon emission reduction paths, and then promote the further reduction of carbon emission. In this paper, the carbon emissions in China and its subordinate provinces during 2005-2017 are firstly divided into four stages, named S1, S2, S3, and S4. The results show that China has just entered the S3, and it is impossible to reach the peak of energy-related CO₂ emissions with steady economic growth before 2030. Then, three-layer LMDI is utilized to explore the drivers of CO₂ emissions, and the impact of urbanization which is separated from the population is considered innovatively. The economic development increases CO₂ emissions, while the other drivers have diverse effects, which may be positive or negative, on carbon emissions in different regions. Therefore, four emission reduction paths with provincial characteristics should be followed in the future: (i) three provinces, namely, Ningxia, Shaanxi, and Xinjiang, should optimize multiple basic objectives in parallel; (ii) four provinces, such as Inner Mongolia and Hainan, should optimize the energy structure; (iii) six provinces, such as Jiangxi and Hunan, should optimize the industry structure; and (iv) the other provinces should develop new clean energy according to regional conditions.

Decoupling analysis of the industrial growth and environmental pollution in the Circum-Bohai-Sea region in China

Based on a comprehensive consideration of waste water (WW) and waste gas (WG), the Tapio decoupling model is constructed to explore the decoupling relationship between industrial growth and industrial pollution in the Circum-Bohai-Sea region (CBSR) of China from 2003 to 2016. By dividing 37 sample cities into three sub-regions, we conduct a comparative analysis to describe the spatial-temporal evolution of the decoupling states of industrial growth and environmental pollution. The results show the following: (1) Overall, the industrial WW discharge in 37 key cities has been decoupled from industrial growth, and the industrial development mode is relatively ideal. (2) The decoupling between industrial growth and industrial WW and WG emissions is more ideal in Beijing-Tianjin-Hebei (BTH) than in Midsouthern Liaoning (MSL). (3) There are two nodes for the decoupling between industrial growth and WW and WG in Shandong Peninsula (SDP), and the decoupling state between industrial growth and WG is better than the decoupling state between industrial growth and WW from 2003 to 2016. (4) From 2003 to 2016, the decoupling state between industrial growth and WW and WG in MSL is not ideal. The conclusions show that the decoupling relationship between industrial growth and environmental pollution in the CBSR is still quite variable and unstable; thus, differential treatment measures should be taken. To enhance the effectiveness of these measures, we will further study the main factors affecting the decoupling relationship, and conduct a comparative study in a larger scale.

Eco-Economic Coordination Analysis of the Yellow River Basin in China: Insights from Major Function-Oriented Zoning

The ecological-economic coordination degree model is widely used to analyze eco-economic coordination relationships, but methods for determining the relative weights of the ecological and economic systems lack a scientific basis. Examining the Yellow River Basin based on Major Function-Oriented Zoning (MFOZ) in China, the study surveyed 42 experts and used the analytic hierarchy process (AHP)to calculate the ecological and economic weights of the different main function zones. It also improved the model and evaluated the coordination degree of the ecological economic system in 642counties of eight provinces in the Yellow River Basin from 1991 to 2015. The results indicate that (1) the ecological value of the basin increased from 823 billion Yuan in 2001 to 1142 billion Yuan in 2015; (2) the GDP shows a linear growth trend: high- and medium–high-value areas of per capita GDP are clustered around nine metropolitan areas, while cold spots are distributed in ecological protection and agricultural development zones; (3) the ecological and economic coordination of the river basin first rose and then declined; and (4) the coordinated development areas are concentrated in five urban agglomerations that are highly consistent with the per capita GDP hotspots.

Climate neutral in agricultural production system: a regional case from China

The concept of climate neutral has been introduced in the agricultural production system to re-examine the connotation of agricultural carbon footprint (CF). According to the integrated accounting framework of the agricultural CF we built, then selected a case from China, and carried out the climate economic effect quantitative analysis of the agricultural production system. The results indicated that CO2 emissions accounted the largest percentage of total carbon emissions by 52.05%, which was driven strongly by the application of agricultural fertilizers and consumption of diesel oil and CH4 emissions (ME) from cattle fed intestinal fermentation, and the driving force behind carbon sequestration was derived from the woody cash crops of carbon sequestration by vegetation and the input of residual carbon from straw returning to field and root stubble in the soil carbon pool. The carbon sink finally realized in the agricultural production system and the agricultural CF index reflected the surplus of 1.801 Mt C in the study area. In addition, we used the indicators of carbon density, carbon intensity, and carbon efficiency to judge the trade-offs of cost-benefit between the agroecosystem and economic system, so as to put forward some potential mitigation strategies for the study area. The mitigative effect of agricultural production system on climate neutral need to be further estimated in a more rigorous manner while controlling for more uncertainties in the future.

Environmental improvement value of agricultural carbon reduction and its spatiotemporal dynamic evolution: Evidence from China

A large increase in carbon emission and other greenhouse gases have attracted much attention around the world for decades. As the second largest carbon emission source in the world, the agricultural carbon emission and the environmental improvement value of agricultural carbon reduction (EIVACR) should not be ignored. Based on the theory of resource economic value, the current study divided 31 provincial-level administrative units of China into three major regions. The Super-Efficiency Slacks-Based Measurement (SBM) model, Moran's I index and Markov chain transfer probability matrix method have been employed to examine EIVACR and it's spatial-temporal dynamic evolution characteristics by adopting panel data of 31 provinces from 1997 to 2017. The result indicated that: (i) during the study period, China's average EIVACR showed significant regional differences, accompanied by the gradually strengthening spatial pattern of the "central > western > eastern"; (ii) the average EIVACR increased from 0.970 Million Yuan in 1997 to 1.164 Million Yuan in 2017, increasing by 20% in 21 years; (iii) no spatial correlation or obvious dependence exist between adjacent provinces, but present a negative impact of "high-low" agglomeration in individual years; (iv) the influencing effects of technology adoption and factor allocation have spatial heterogeneity, and the influencing effect of policy orientation has temporal heterogeneity. Therefore, differentiated carbon reduction policies should be formulated according to regional and temporal differences. Meanwhile, carbon reduction market trading mechanism and compensation policy should be established. What's more, regional cooperation needs to be strengthened, to form a synergistic carbon reduction effect.

The effect of trade openness on the relationship between agricultural technology inputs and carbon emissions: evidence from a panel threshold model

The development of low-carbon agriculture systems has been a global consensus to reduce carbon emissions in the agricultural sector for addressing climate change challenges. This fact brings the need to study the agricultural carbon emissions (ACEs). Studies focusing on calculating the spatiotemporal changes of ACEs and analyzing the main factors for ACE changes have been conducted. The agricultural technology inputs (ATIs) as an important factor to influence ACEs have been identified. The traditional linear model was the commonly used method to study the relationship between ATIs and ACEs, whereas the impact of ATIs on ACEs in different areas might be complex and nonlinear due to the differences in trade openness causing different development levels of agricultural technologies. Therefore, this study aims to investigate the effect of trade openness on the relationship between ATIs and ACEs using a panel threshold model and put forward policy implications for the low-carbon agriculture development. The analysis was based on data from a panel of 31 provinces of China during 2003-2018. The results show that ATIs and ACEs increased from 2003 to 2018 and the spatial distribution of ATIs was similar to that of ACEs. The ATIs had a positive effect on ACEs with a significant single-threshold effect from trade openness. When the trade openness was below the threshold (0.1425), the positive effect of ATIs on ACEs was significant (coefficient, 0.117), whereas, when the trade openness was above the threshold (0.1425), the positive effect of ATIs on ACEs significantly decreased (coefficient, 0.062). Furthermore, industrial structure and agricultural economic development were the positive drivers of ACEs, while trade openness, education level of rural workers, R&D funding, and natural disasters had negative relationships with ACEs. The results provide valuable references for understanding ACE drivers and developing low-carbon agriculture with the consideration of ATIs and trade openness.

The influencing factors and spillover effects of interprovincial agricultural carbon emissions in China

Agricultural carbon emissions have become the constraints of agricultural low-carbon and circular economy development in China. China’s agricultural production faces severe pressures and challenges in agricultural carbon reduction. In this paper, we take observation for the 31 provinces in china from 1997 to 2017, to explore the influencing factors and spatial spillover effects of agricultural by estimating spatial panel data models. The results show that China’s agricultural carbon emissions will continue to increase in the future, because the growth of per capita gross domestic product (GDP) is the main driving force to accelerate the growth of agricultural carbon emissions, but the agricultural input factors will help to reduce the growth of carbon emissions. Moreover, it is proved that economic factors and agricultural input factors have direct effects and spatial spillover effects on agricultural carbon emissions except for agricultural environmental factors. In the short term, strengthening environmental protection may bring some pressure to the economic development of some places, but to achieve high-quality development, we must fundamentally solve the problem of environmental pollution. The conclusion provides important enlightenment and scientific basis for formulating effective policies to curb the growth of CO₂ emissions in China.

Decoupling and mitigation potential analysis of CO2 emissions from Pakistan's transport sector

The transport sector has become one of the major economic, huge fossil fuel energy consumption, and carbon dioxide (CO₂) emitting sector of Pakistan. This study applies the logarithmic mean Divisia index (LMDI) and Tapio's decoupling approach to estimate decoupling state and mitigation potential of CO₂ emissions from the transport sector during 1984-2018. LMDI technique is applied to detect the influencing variables (i.e. carbon coefficient, fuel consumption, total energy consumption, and turn over economy), which oversee CO₂ emissions. The outcomes show that CO₂ coefficient effect is the factor which is decreasing CO₂ emissions while economic growth (EG) effect is the factor which is growing CO₂ emissions. The decoupling index is also applied to influencing factors which reflect the EG factors on CO₂ emissions from the transport sector. The consequences confirm that during 1984-2018, the CO₂ emissions show an expensive coupling with EG. Weak decoupling occurred only in the sub-periods 1999-2003, 2004-2008, and 2009-2013. Similarly, the CO₂ emissions occurred from only three decoupling grades. Furthermore, a mitigation model based on the above impacting variables estimates the mitigation rate of CO₂ emissions and showed that the CO₂ mitigation seemed in 1999-2003, 2004-2008, and 2009-2013. Finally, forecasting outcomes of Tapio decoupling index show a weak decoupling during 2018-2030. Therefore, based on the empirical outcomes, this study puts forward a few policy suggestions to efficiently enhance the decoupling between Pakistan's transport CO₂ emissions and EG.

Exploring environmental efficiency and total factor productivity of cultivated land use in China

During cultivated land use, nonseparable relationships exist between certain inputs and outputs. This study explored the influence of the nonseparable characteristics of input and output variables on both the cultivated land use environmental efficiency (CLUEE) and the cultivated land use environmental total factor productivity (CLUETFP). To evaluate China's CLUEE and CLUETFP from 1997 to 2017, we used the nonseparable hybrid model with undesirable outputs (NSH-U) and the nonseparable hybrid Malmquist (NSH-M) productivity index. The results showed the following: (1) The CLUEE differed significantly among regions, with the CLUEE decreasing from the eastern region to the western, northeastern, and central regions. We observed large differences in the CLUEE among provinces. (2) Nonradial input inefficiency and radial output inefficiency were the primary sources of cultivated land use environmental inefficiency. (3) Technical progress was the primary driving force behind the growth of CLUETFP across the entire country and in the eastern, central, western, and northeastern regions; however, technical efficiency limited the growth of CLUETFP to a certain extent. Finally, we proposed policy implications to improve China's CLUEE and CLUETFP.

Carbon emissions, the industrial structure and economic growth: Evidence from heterogeneous industries in China

A comprehensive understanding of the relationships among carbon emissions, the industrial structure and economic growth holds great significance for China's transition to a low-carbon economy, industrial structure optimization, and achievement of energy conservation and emission reduction targets. We selected six major industrial sectors (agriculture, industry, construction, transportation, retail and accommodation and other industries) as research objects, introduced the extended STIRPAT decomposition model, Tapio decoupling model and the grey relation analysis to discuss the relationship among the three. Results showed that (i) since 2000, the proportions of value added of agriculture, manufacturing, and transportation are negatively correlated with carbon emissions, while those of construction, retail and accommodation, and other industries are positively correlated with carbon emissions. (ii) The overall economic growth and carbon emissions of these six major industries have experienced the process of decoupling-coupling-decoupling-coupling-decoupling. (iii) The relevance of these six industries to GDP is ranked as follows: transportation > manufacturing > retail andaccommodation > agriculture > construction > other industries. Additionally, accelerating the achievement of a clean energy structure, strengthening the strength and speed of industrial structure adjustment and reducing the dependence on fossil energy are the key steps for China to reach carbon emissions peak goal.

A regional-scale decomposition of energy-related carbon emission and its decoupling from economic growth in China

China, known as the largest carbon emitter and the second largest economy worldwide, has continued to put effort into the understandings of the main drivers of carbon emission and their decoupling statuses from its economic growth. Considering the significant differences of natural and social environments in different regions of China, this paper presents a regional-scale decomposition of energy-related carbon emission and its decoupling from economic growth by using the Logarithmic Mean Divisia Index (LMDI) and the Tapio decoupling method. The decoupling results indicate that carbon emissions in all regions show a stable decoupling trend from their economic development, which means that China is now on the right road for achieving a low-carbon economy. However, the decoupling status by the end of 2016 also indicates that most of the regions are still in the states of expansive coupling or weak decoupling, especially in Northwest (NW), which implies that the speed of decarbonization process is still not high enough. The decomposition results show that in all regions except NW, GDP per capita is the most influential factor leading to increasing carbon emissions, while energy intensity is the largest factor in reducing carbon emissions. In NW, both GDP per capita and energy intensity drive the increase in carbon emissions. The results in this paper could benefit China's regional policy-making and national strategies.

Whether China made efforts to decouple economic growth from CO2 emissions?-Production vs consumption perspective

Decoupling analysis is able to reveal the linkage between economic growth and environmental pressure. However, traditional studies mostly concentrate on production-based decoupling analysis and ignore the pressure emerging from supply chains to satisfy the final consumption. Through a comprehensive framework integrating input-output analysis, decomposition methods, and the Tapio index, this work may be considered the first attempt to explore whether China made efforts to decouple economic growth from CO₂ emissions from production-based and consumption-based perspectives simultaneously. We found that (1) CO₂ emissions in China expanded by around 1.6-fold during 2002-2015, of which Production and supply of electricity and heat and Construction contributed most to the production-based emissions (PBE) and consumption-based emissions (CBE), respectively; (2) Three-quarters of sectors presented weak decoupling or strong decoupling under both PBE and CBE perspectives, and Textile was the only sector achieving strong decoupling under both perspectives; (3) All sectors have made efforts to decouple economic growth from CO₂ emissions under PBE perspective, while several sectors failed under CBE perspective. Overall, the decoupling status for PBE was better than that for CBE during the study period. Our results are able to provide targeted and effective references for allocating decoupling responsibilities between producers and final consumers more adequately and reasonably.

Feasibility of peaking carbon emissions of the power sector in China's eight regions: decomposition, decoupling, and prediction analysis

Carbon emissions in the power sector are an important part of China's total carbon emissions and have a significant impact on whether China can achieve the 2030 carbon peak target. Based on the three perspectives of decomposition, decoupling, and prediction, this paper studies the feasibility of carbon emission peaks in eight major regional power sectors in China. First, the generalized Divisia index model (GDIM) is used to decompose the carbon emissions of the eight regional power sectors, and the driving factors and their effects on carbon emissions in the power sector of each region are compared. Then, the decoupling index based on the generalized Divisia index model (GDIM-D) is used to study the decoupling relationship between the carbon emissions of the eight regional power sectors and economic growth. Finally, the carbon emissions and decoupling indices of the power sector from 2017 to 2030 are predicted. The results show the following. First, the gross domestic product (GDP) and output scale are the main factors contributing to the carbon emissions of the eight regional power sectors. The carbon intensity of the power sector in GDP (C/G) and output carbon intensity(C/E) are the main factors that contribute to the reduction. Second, the carbon emissions of the southern coast, the middle Yellow River, and the Southwest peaked in 2013 and have been decoupled from economic growth, while those in the other regions have not peaked or decoupled. Third, if the carbon emissions of the power sector in the Northeast, northern coast, eastern coast, middle Yangtze River, and Northwest reach a peak in 2030, they will face many emission reduction pressures. This paper provides a reference for studying the carbon emissions of China's regional power sectors and their relationship with economic growth and has important implications for peak carbon emissions at the national level.

Systematic analysis of the coordination degree of China's economy-ecological environment system and its influencing factor

In order to achieve a sustainable development of economy and ecological environment, starting from the systemic thinking and systemic approach, this paper regards China's economic development and ecological environment protection as a whole system. Firstly, the negative index of M2 is introduced to correct the GDP bubble, and constructed the architecture model of the economy-ecological environment system. Then, this research establishes an evaluation index system using the analytic hierarchy process and Yaahp software and obtains its comprehensive evaluation value. After that, this research builds the Lotka-Volterra coordination degree model and uses the China data from 1997-2016 to analyze the mutual influencing factors and coupling coordination degree, and carries out the empirical and experimental tests to obtain the overall coordination relationship between economy and ecological environment systems. The results of the study show that the trend of economy-ecological environment coordination degree in China has changed from relatively coordinated to moderately uncoordinated in the latter 20 years; the coordination degree decreased from 0.8996 to 0.4842. Although, the situation has a rebound in the latter 2 years, the situation is still not optimistic. In addition, even the modified model of economic subsystem plus M2 did not change the original changing trend, and only changed the magnitude of the gap, thus revealing that the imbalance of economic development is much more serious than the impact of overissue currency on the ecological environment. This study provides a new basis for research decisions such as the adjustment of the economic growth rate and the optimization of ecological environment.

Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China

With the development of agricultural modernization, the carbon emissions caused by the agricultural sector have attracted academic and practitioners’ circles’ attention. This research selected the typical agricultural development province in China, Fujian, as the research object. Based on the carbon emission sources of five main aspects in agricultural production, this paper applied the latest carbon emission coefficients released by Intergovernmental Panel on Climate Change of the UN (IPCC) and World Resources Institute (WRI), then used the ordered weighted aggregation (OWA) operator to remeasure agricultural carbon emissions in Fujian from 2008–2017. The results showed that the amount of agricultural carbon emissions in Fujian was 5541.95 × 103 tonnes by 2017, which means the average amount of agricultural carbon emissions in 2017 was 615.78 × 103 tonnes, with a decrease of 13.13% compared with that in 2008. In terms of spatial distribution, agricultural carbon emissions in the eastern coastal areas were less than those in the inland regions. Among them, the highest agricultural carbon emissions were in Zhangzhou, Nanping, and Sanming, while the lowest were in Xiamen, Putian, and Ningde. In addition, this paper selected six influencing variables, the research and development intensity, the proportion of agricultural labor force, the added value of agriculture, the agricultural industrial structure, the per capita disposable income of rural residents, and per capita arable land area, to clarify further the impacts on agricultural carbon emissions. Finally, geographically- and temporally-weighted regression (GTWR) was used to measure the direction and degree of the influences of factors on agricultural carbon emission. The conclusion showed that the regression coefficients of each selected factor in cities were positive or negative, which indicated that the impacts on agricultural carbon emission had the characteristics of geospatial nonstationarity.

Probing the affecting factors and decoupling analysis of energy industrial carbon emissions in Liaoning, China

With the revitalization of the old industrial bases in Northeast China, the development of the energy industry is particularly significative. The purpose of this paper is analyzing the decoupling of carbon emissions and placing emphasis on Liaoning's energy industry. The researchers used the logarithmic mean Divisia index (LMDI) decompose model to take into account carbon emissions in each energy industry and used the Tapio decoupling model from 2000 to 2015 to seek the decoupling states. The main completion of this study are as follows: (1) The EGH and OPC industry are the dominating components of the carbon emissions of the energy industry. The coal and crude oil accounted for 95% of energy industrial consumption; there is great potential for electricity to replace coal and crude oil. (2) The direction of the changes in economic growth, investment structure, investment dependence, and energy structure is the same as industrial carbon emissions. Meanwhile, energy intensity and energy technology are the opposite during the period. (3) The CMW and PGE industry occurred strong decoupling between carbon emissions and economic output since 2005; there is weak decoupling state in other industry. And the PGE and OPC industry occurred recessive coupling and weak negative decoupling between carbon emissions and energy intensity except 2011 and 2012.