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

Can the development of the rural digital economy reduce agricultural carbon emissions? A spatiotemporal empirical study based on China's provinces

Rapid advancement of the rural digital economy has intensified the demand for leveraging digital tools to foster low-carbon and sustainable agricultural practices, garnering widespread academic and bureaucratic attention. Understanding how the rural digital economy influences agricultural carbon emissions is crucial for unlocking emission reduction potential, facilitating a transition towards sustainable energy usage in rural areas, and nurturing green agricultural development. In this study, we employ the entropy method, a spatial Durbin model, and a panel threshold model to assess the impact of the rural digital economy on agricultural carbon emissions across each province in China from 2010 to 2022. Additionally, we delve into the mechanism through which the rural digital economy facilitates agricultural carbon reduction, particularly in terms of "agricultural socialized services". Our findings reveal several key insights. Firstly, the rural digital economy contributes significantly to reducing agricultural carbon emission intensity. Secondly, there is a non-linear relationship between the rural digital economy and agricultural carbon emissions. With the development of rural digital economy showing a marginal decreasing trend, there is an obvious threshold effect. Thirdly, enhancing agricultural socialized services through the rural digital economy can curb agricultural carbon emissions. Lastly, the carbon reduction effect of the rural digital economy is more significant in more economically developed areas, areas with moderate levels of economic development, and areas with low technological investment; implementation of a "zero growth" policy for fertilizers strengthens this carbon reduction effect. This study sheds light on the mechanisms and effects of agricultural carbon emissions, offering quantitative evidence and theoretical support for the transition towards low-carbon and sustainable agricultural development.

Potential benefits of cropping pattern change in the climate-sensitive regions of rice production in China

Rice production is a primary contributor to global greenhouse gas emissions, with unclear pathways towards carbon neutrality. Here, through a comprehensive assessment of direct greenhouse gas (GHG) emission using DNDC model and indirect GHG emission using emission factor methods, we estimated the annual crop yield, GHG emission amount and intensity, and economic benefits of different cropping patterns in the climate-sensitive regions of rice production in China. Through the expansion of single-rice and cropping pattern change from the wheat-rice to wheat-rice-rice in the climate-sensitive regions of single and triple-cropping cultivations, the total grain yield increased by 4.4 % and 4.5 % compared with the current national grain production, the GHG emission would increase by 2.4 % and 5.4 % of the current national GHG emissions from rice and wheat production, the net economic benefits could increase 0.9 % and decrease 2.0 % of the national output value of rice and wheat production. The study takes the entire-life cycle of crop growth as the principal line, and could provide a valuable reference for the regulation of the cropping pattern and the formulation of carbon reduction policies in the climate-sensitive region.

Does carbon reduction and sequestration conflict with food security in rural China?-What, why and how?

Based on panel data of 31 provinces in rural China from 1997 to 2020, this manuscript first applies a carbon reduction and sequestration (CRS) model from the perspective of agricultural carbon emissions and agricultural carbon sinks. We then construct a food security evaluation system to examine the four dimensions of quantity, quality, ecological and economic security. Finally, the study uses a spatial Durbin model to empirically analyze the impact of CRS on food security and the moderating effect of fiscal decentralization. The relevant results: First, from 1997 to 2020, carbon emissions rose from 221.9794 million tons (1997) to 251.1368 million tons (2020), representing an increase of 13.14 %. The total amount of carbon sinks increased from 518.259 million tons (1997) to 758.887 million tons (2020); an increase of 46.43 %. CRS exhibited a fluctuating downward trend, falling from 0.98 (1997) to 0.90 (2020). However, food security showed an increasing trend, rising 0.12 (1997) to 0.32 (2020), with an average annual growth rate of 6.94 %. Second, in the short term, national CRS has had a significantly negative impact on food security, whereas the long term the result is exactly the opposite. In terms of control variables, planting structure, openness to the world, and economic development have significantly positive impact on food security, and urbanization, technological progress, and environmental regulation have significantly negative impact on food security. Regional heterogeneity is evident in the three functional attribute areas. Third, fiscal decentralization can enhance the negative impact of CRS on food security in the short term and weaken the positive impact of CRS on food security in the long term. Similarly, some regional heterogeneity is found among different regions.

How does urbanization impact China's carbon emissions: A regional heterogeneity perspective

In the context of China's green development and "dual carbon" goal, urbanization, as a way to achieve Chinese modernization, has a particularly important effect on green and low-carbon economic development. Firstly, this paper empirically analyzed the influence of urbanization on per capita carbon emissions using Chinese city data and a panel fixed-effects model. Then, the impact mechanisms of urbanization on carbon emissions were examined from both the demand and supply sides. Finally, we analyzed the differences in the transmission mechanisms of urbanization affecting carbon emissions in the eastern, central, and western regions. The results show that (1) urbanization increases per capita carbon emissions. However, this effect shows inter-regional differences, with more significant promotion effects in the eastern and central regions; (2) on the demand side, the residents' consumption intensity can drive carbon emissions, while the rise of human capital agglomeration suppresses carbon emissions; on the supply side, industrial structure can drive carbon emissions, while the increase of green technological innovation suppresses carbon emissions; (3) the consumption effect and the industry effect play a major role in the eastern and central regions, while the intermediary effect is not obvious in the western region. This study can provide important insights for synergizing urbanization and achieving carbon reduction commitments.

Comprehensive assessment of refined greenhouse gas emissions from China's livestock sector

Livestock and poultry products are an essential human food source. However, the rapid development of the livestock sector (LS) has caused it to become a significant source of greenhouse gas (GHG) emissions. Consequently, investigating the spatio-temporal characteristics and evolution of GHG emissions is crucial to facilitate the green development of the LS and achieve "peak carbon and carbon neutrality". This study combined life cycle assessment (LCA) with the IPCC Tier II method to construct a novel GHG emissions inventory. The GHG emissions of 31 provinces in China from 2000 to 2021 were calculated, and their spatio-temporal characteristics were revealed. Then, the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model was used to identify the main driving factors of GHG emissions in six regions of China and explore the emission reduction potential. The results showed that GHG emissions increased and then decreased from 2000 to 2021, following a gradual and steady trend. The peak of 628.55 Mt CO2-eq was reached in 2006. The main GHG-producing segments were enteric fermentation, slaughtering and processing, and manure management, accounting for 45.39 %, 26.34 %, and 23.08 % of total GHG emissions, respectively. Overall, the center of gravity of GHG emissions in China migrated northward, with spatial aggregation observed since 2016. The high emission intensity regions were mainly located west of the "Hu Huanyong line". Economic efficiency and emissions intensity were the main drivers of GHG emissions. Under the baseline scenario, GHG emissions are not projected to peak until 2050. Therefore, urgent action is needed to promote the low-carbon green development of the LS in China. The results can serve as scientific references for the macro-prevention and control of GHG emissions, aiding strategic decision-making. Additionally, they can provide new ideas for GHG accounting in China and other countries around the world.

Towards climate neutrality in the Spanish N-fertilizer sector: A study based on radiative forcing

Agricultural systems in the 21st Century face the double challenge of achieving climate neutrality while maintaining food security. Synthetic fertilizers rich in nitrogen (N-fertilizers) boost agricultural production at the expense of increasing climate impact. Public policies, such as the Farm-to-Fork (F2F) Strategy, aim to reduce the extensive use of N-fertilizers with the ultimate goal of achieving a climate neutral European Union (EU). However, the strong link between N-fertilizers and GHG emissions (i.e., CO2, CH4 and, especially, N2O) highlights the need to better understand the climate impact of this sector. The present study conducts a climate impact analysis of Spanish N-fertilizer sector for two periods: (i) from 1960 to 2020 using real data and (ii) from 2021 to 2100 considering five forecasted scenarios. The scenarios range from business-as-usual practices to a full accomplishment of the goals pursued by the EU's F2F strategy. The system's climate stability and neutrality are analysed for the different scenarios based on radiative forcing (RF) metrics. Additionally, the study evaluates the short-term impact of the EU decarbonization goals on the climate impact of the Spanish N-fertilizer sector. The results of the study illustrate that the long-lasting climate impact of N2O and CO2 emissions compromise the capacity of N-fertilizer sector to achieve climate stability and approach climate neutrality. However, the decarbonisation of transport and N-fertilizer production activities is an important driver to substantially reduce the life cycle CH4 and CO2 emissions in the Spanish N-fertilizer sector. The results also highlight that more severe reductions on N-cycles than those suggested by the EU's F2F are required, especially to reduce the long-lasting N2O emissions in the N-fertilizer sector. Overall, the study concludes that using RF-based metrics increases robustness and transparency of climate assessments, which is necessary for a higher integration of climate science within public policymaking.

Carbon emissions changes of animal husbandry in China: Trends, attributions, and solutions: A spatial shift-share analysis

China is a major livestock producer confronting the dual challenges of rising demand for animal-based food consumption and decreasing carbon emissions. To effectively address these issues, it is crucial to understand the trends of carbon emissions from animal husbandry and the competitive advantages of carbon emission reduction in different regions. This study uses panel data from 31 provinces from 2004 to 2020 to investigate the contributing factors to carbon emissions and explore ways to reduce carbon intensity in animal husbandry. The analysis employs spatial shift-share analysis and the spatial Durbin model. Our findings indicate that life-cycle carbon emissions associated with animal husbandry in China decreased from 572.411 Mt CO2eq to 520.413 Mt CO2eq over time, with an average annual decline of 0.568 %. The annual contribution of output value and internal industry-mix adjustment to carbon emission growth is 22.639 MT CO2eq and 6.226 MT CO2eq, respectively. On the other hand, the annual contribution of carbon efficiency improvement to carbon emission reduction is much higher, at 36.316 MT CO2eq. However, there is significant regional heterogeneity in the spatial decomposition of the carbon efficiency change component. The Northeastern region, Northwest and along the Great Wall demonstrate neighborhood advantages in enhancing carbon efficiency. In contrast, the South China and Southwest regions rely more on local carbon efficiency advantages to reduce the carbon intensity of animal husbandry. Furthermore, the carbon intensity in local and neighboring areas can be reduced through environmental regulations and industrial agglomeration. While technical progress significantly negatively impacts carbon intensity in neighboring regions, it does not contribute to reducing the carbon intensity of local animal husbandry. The findings provide valuable insights for local governments, aiding them in recognizing the pros and cons of carbon reduction in animal husbandry and strengthening regional cooperation in emission reduction management.

Exploring the dual impact: Dissecting the impact of tourism agglomeration on low-carbon agriculture

Despite extensive research on the relationship between tourism and agriculture, the specific impact of tourism on agriculture's low-carbon transition has not been thoroughly investigated. This study analyzes the effects of tourism agglomeration on agricultural carbon intensity across 30 Chinese provinces from 2001 to 2020. It is framed within the context of rural digitalization, with a particular emphasis on the integration of agro-tourism and the total factor productivity of agriculture. Utilizing spatial econometric models, we find that tourism agglomeration hinders the low-carbon transition in agriculture by influencing carbon intensity both directly and indirectly. At the national level, the impact of tourism agglomeration follows an inverted-U curve with respect to agro-tourism integration and carbon intensity. At the regional level, the effects vary, with weaker indirect influences in major grain-producing areas. Furthermore, rural digitalization appears to lessen the adverse impacts of tourism on carbon intensity. This study also identifies significant spatial spillover effects from tourism agglomeration. The findings suggest that provinces with high tourist influx should enhance investments in climate-smart agricultural practices and technologies to counteract these negative impacts. Moreover, integrated governance of tourism and agriculture is essential for achieving carbon neutrality in both sectors.

A factorial-analysis-based Bayesian neural network method for quantifying China's CO2 emissions under dual-carbon target

Energy-structure transformation and CO2-emission reduction are becoming particularly urgent for China and many other countries. Development of effective methods that are capable of quantifying and predicting CO2 emissions to achieve carbon neutrality is desired. This study advances a factorial-analysis-based Bayesian neural network (abbreviated as FABNN) method to reflect the complex relationship between inputs and outputs as well as reveal the individual and interactive effects of multiple factors affecting CO2 emissions. FABNN is then applied to analyzing CO2 emissions of China (abbreviated as CEC), where multiple factors involve in energy (e.g., the consumption of natural gas, CONG), economic (e.g., Gross domestic product, GDP) and social (e.g., the rate of urbanization, ROU) aspects are investigated and 512 scenarios are designed to achieve the national dual carbon targets (i.e., carbon peak before 2030 and carbon neutrality by 2060). Comparing to the conventional machine learning methods, FABNN performs better in calibration and validation results, indicating that FABNN is suitable for CEC simulation and prediction. Results disclose that the top three factors affecting CEC under the dual‑carbon target are GDP, CONG, and ROU; energy, economic and social contributions are 43.5 %, 34.6 % and 21.9 %, respectively. CEC reaches its carbon peak during 2027-2032 and achieve carbon neutrality during 2053-2057 under all scenarios. Under the optimal scenario (S195), the CO2-emission reduction potential is about 772.2 million tonnes and the consumptions of coal, petroleum and natural gas can be respectively reduced by 3.1 %, 9.9 % and 23.0 % compared to the worst scenario (S466). The results can provide solid support for national energy-structure transformation and CO2-emission reduction to achieve carbon-peak and carbon-neutrality targets.

Re-measurement and influencing factors of agricultural eco-efficiency under the 'dual carbon' target in China

Given that agriculture is both a carbon source and sink, the sustainability goals of carbon peaking and neutrality place high demands on the green and low-carbon agricultural development in China, and the exploration of a realistic path for a sustainable agricultural development is urgently needed. Under the above 'dual carbon' target, this study focused on the key issue of how to improve China's agricultural eco-efficiency (AEE) and constructed an innovative AEE indicator system that can reflect carbon constraint and coordinated agricultural economic development, resource use and ecological environment. The super-efficient slack-based measured Data Envelopment Analysis (SBM-DEA) method, which considers undesirable outputs, was applied to re-measure the AEE of 30 provinces and cities in China from 2001 to 2020, and its spatial and temporal evolution was analysed in conjunction with kernel density estimation. The Tobit regression model was used to explore various influencing factors by region. The results show that the AEE re-measurements, which take into account the 'dual carbon' requirement, are significantly better than the traditional AEE. From 2001 to 2020, China had an overall V-shaped fluctuation curve AEE, with a small decline and several inter-annual fluctuations, and exhibited a large potential to rise. China's AEE showed a spatially uneven regional development at different stages of distribution and evident multi-polar differentiation. Inter-provincial differences were observed in China's AEE, and the vicious circle of low-level green and low-carbon agricultural development was difficult to break. Urbanisation had a significant positive effect on national and eastern AEE but a significant negative effect on central AEE. The agricultural carbon offset rate had a significant effect on AEE nationally and in the three regions. Thus, the introduction of 'dual carbon' target effectively drove the development of AEE. Agricultural industry structure inhibited the improvement of AEE nationally and in the western region. Agricultural economic development hindered the national AEE improvement but promoted that of the central region, where China showed an environment Kuznets curve. Hopefully, this study can provide data support and theoretical reference for the green and low-carbon agricultural development and help achieve the 'dual carbon' target.

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.

Trends and driving forces of agricultural carbon emissions: A case study of Anhui, China

To facilitate accurate prediction and empirical research on regional agricultural carbon emissions, this paper uses the LLE-PSO-XGBoost carbon emission model, which combines the Local Linear Embedding (LLE), Particle Swarm Algorithm (PSO) and Extreme Gradient Boosting Algorithm (XGBoost), to forecast regional agricultural carbon emissions in Anhui Province under different scenarios. The results show that the regional agricultural carbon emissions in Anhui Province generally show an upward and then downward trend during 2000-2021, and the regional agricultural carbon emissions in Anhui Province in 2030 are expected to fluctuate between 11,342,100 tones and 14,445,700 tones under five different set scenarios. The projections of regional agricultural carbon emissions can play an important role in supporting the development of local regional agriculture, helping to guide the input and policy guidance of local rural low-carbon agriculture and promoting the development of rural areas towards a resource-saving and environment-friendly society.

Spatiotemporal characteristics and influencing factors of carbon emissions from land-use change in Shaanxi Province, China

Due to global warming, there evolves a global consensus and urgent need on carbon emission mitigations, especially in developing countries. We investigated the spatiotemporal characteristics of carbon emissions induced by land use change in Shaanxi at the city level, from 2000 to 2020, by combining direct and indirect emission calculation methods with correction coefficients. In addition, we evaluated the impact of 10 different factors through the geodetector model and their spatial heterogeneity with the geographic weighted regression (GWR) model. Our results showed that the carbon emissions and carbon intensity of Shaanxi had increased overall in the study period but with a decreased growth rate during each 5-year period: 2000-2005, 2005-2010, 2010-2015, and 2015-2020. In terms of carbon emissions, the conversion of croplands into built-up land contributed the most. The spatial distribution of carbon emissions in Shaanxi was ranked as follows: Central Shaanxi > Northern Shaanxi > Southern Shaanxi. Local spatial agglomeration was reflected in the cold spots around Xi'an, and hot spots around Yulin. With respect to the principal driving factors, the gross domestic product (GDP) was the dominant factor affecting most of the carbon emissions induced by land cover and land use change in Shaanxi, and socioeconomic factors generally had a greater influence than natural factors. Socioeconomic variables also showed evident spatial heterogeneity in carbon emissions. The results of this study may aid in the formulation of land use policy that is based on reducing carbon emissions in developing areas of China, as well as contribute to transitioning into a "low-carbon" economy.

Economic, environmental and social efficiency and effectiveness development in the sustainable crop agricultural sector: A systematic in-depth analysis review

Multi-dimensional inclusion of economic, environmental, and social sustainability spheres together are the most global concerns of the agricultural crop sector. Therefore, optimizing waste and natural resources guides researchers and policymakers to structure actions and strategies to attain sustainability. Several studies have been published around the world to choose between focusing on eco-efficiency or eco-effectiveness in different aspects. This work aims to systematically apply an updated review to critically assess the agricultural research articles' contributions among the assessment of those methods, models or tools, as well as a quantitative and qualitative in-depth analysis review to classify them, according to their mapping, functions, strengths, weaknesses, and logical relationships for the evaluation in the crop agricultural sector, which is expected to be needed in future to better understand the research gaps and select the appropriate methods for sustainability evaluation from different spheres (ecology, economy, and sociology). Of 242 peer-reviewed records from 2018 to the beginning of 2023, 135 reviews and articles gathered from Web of Science and Scopus meet the criteria to be examined. Our analysis revealed that the number of reviews is limited to approximately 4.5 %; most of the case studies were carried out in countries, such as China (36 %) and Brazil (6 %), and continents such as Europe (16 %). Depending on considered aspects, most studies evaluate the efficiency, effectiveness and derivatives using a set of tools, varying between the managerial tools applied for the macro-level structuration (DPSIR, EMA, and LCA) and mathematical tools applied for the micro-level quantification, subdivided into the visualization methods (GIS), and the optimization methods (DEA, SFA, MILP, FO). Thanks to their multifunctionality in considering different aspects of input, output and influence factors variables, the in-depth analysis study suggests the application of data envelopment and stochastic analysis to carry out a multidisciplinary evaluation for the socio-eco-efficiency or the socio-eco-effectiveness.

Analysis of the spatial-temporal evolution of Green and low carbon utilization efficiency of agricultural land in China and its influencing factors under the goal of carbon neutralization

Agricultural land is the most basic input factor for agricultural production and an essential component of terrestrial ecosystems, which plays a vital role in achieving carbon neutrality. Giving full play to the carbon-neutral contribution of agricultural land is a crucial part of China's economic transformation and green development. It incorporates carbon and pollution emissions from agricultural land use into the unexpected outputs of the Green and Low-carbon Utilization Efficiency of Agricultural Land (GLUEAL) evaluation system. The study utilized several advanced analytical tools, including the super-efficient Slacks-Based Measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method, Geodetector, and Geographically and Temporally Weighted Regression (GTWR) model. The objective was to examine the spatial-temporal evolution of GLUEAL and identify the factors that influenced it in all 31 provinces of China from 2005 to 2020. The results show that: (1) The overall spatial-temporal evolution of GLUEAL showed an increasing trend, but the disparity between provinces and regions became wider. (2) Most provinces have not yet made significant spatial and temporal jumps. They have high spatial cohesion with specific "path-dependent" characteristics. (3) The Geodetector results reveal that the Number of Rural Labor Force with Higher Education (NRLFHE) and Technology Support for Agriculture (TSA) have insufficient explanatory power on average for GLUEAL. Agricultural Economic Development Level (AEDL), Urbanization Level (UL), Multiple Crop Index (MCI), Planting Structure (PS), Degree of Crop Damage (DCD), Financial support for agriculture (FSA), and Agricultural mechanization level (AML) had stronger explanatory power on average for GLUEAL and were important factors influencing GLUEAL levels. (4) The average influence of AEDL, UL, FSA, and AML on GLUEAL changed from negative to positive. The average influence of MCI and DCD on GLUEAL was negative, and the average influence of PS on GLUEAL changed from positive to negative. This study provides a comprehensive description of the spatial and temporal evolution of GLUEAL in China. It reveals the key factors influencing GLUEAL and analyzes their spatial variations and impact patterns. These findings offer robust evidence for government policymakers to formulate policy measures for sustainable agricultural development and optimized resource allocation, promoting the transformation of agricultural land towards green and low-carbon practices and advancing the achievement of sustainable development goals.

Influencing factors and spatiotemporal heterogeneity of net carbon sink of conservation tillage: evidence from China

Conservation tillage is an important reform of traditional tillage, which has significant carbon sequestration and emission reduction effects. It is important to investigate the influencing factors and spatiotemporal heterogeneity of net carbon sink of conservation tillage for realizing the "dual carbon" target, and facilitating agricultural sustainable development. This study used the coefficient accounting method to calculate the carbon sink and carbon emission of conservation tillage in China from 2000 to 2019, respectively. Based on this, the net carbon sink of conservation tillage was measured. Then, the spatiotemporal heterogeneity of influencing factors on net carbon sink of conservation tillage was analyzed by using the geographically and temporally weighted regression model. The results showed that (1) the net carbon sink of conservation tillage in China was significant and had potential to have a constant rise; (2) spatially, the net carbon sink of conservation tillage changed more variably in longitudinal direction. Specifically, the promotion effect of conservation tillage machinery gradually decreased from west to east. The planting structure and conservation tillage promotion intensity played key roles in improving net carbon sink of conservation tillage. (3) Temporally, the effect of conservation tillage machinery showed positive effect of decreasing yearly, while the positive effect of promotion intensity increased year by year. Planting structure and economic development negatively affected improvement on the net carbon sink of conservation tillage and the negative effect increased year by year. Additionally, the effect of education on the net carbon sink shifted from positive to negative over time. The study aims to provide a reference for the government to promote conservation tillage according to local conditions and to achieve the "dual carbon" target.

A review of applied research on low-carbon urban design: based on scientific knowledge mapping

The construction of low-carbon cities is an essential component of sustainable urban development. However, there is a lack of a comprehensive low-carbon city design and evaluation system that incorporates "carbon sink accounting-remote sensing monitoring-numerical modelling-design and application" in an all-around linkage, multi-scale coupling, and localized effects. This paper utilizes the Citespace tool to evaluate low-carbon city design applications by analyzing literature in the Web of Science (WOS) core collection database. The results reveal that low-carbon cities undergo four stages: "measurement-implementation-regulation - management." The research themes are divided into three core clustering evolutionary pathways: "extension of carbon sink functions," "spatialisation of carbon sink systems," and "full-cycle, full-dimensional decarbonisation." Applications include "Utility studies of multi-scale carbon sink assessments," "Correlation analysis of carbon sink influencing factors," "Predictive characterisation of multiple planning scenarios," and "Spatial planning applications of urban sink enhancement." Future low-carbon city construction should incorporate intelligent algorithm technology in real-time to provide a strong design basis for multi-scale urban spatial design with the features of "high-precision accounting, full-cycle assessment and low-energy concept."

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.

Analysis of the factors influencing the water-energy-food system stress in China

Water, energy and food security are at the heart of the UN 2030 Agenda for Sustainable Development. Maintaining water-energy-food (WEF) system security is critical to sustainable socio-economic development. To clarify the trends in China's WEF system stress, this paper analyses the spatial and temporal heterogeneity of WEF system stress using panel data for 30 Chinese provinces from 2002 to 2020. Using an extended STIRPAT model, we discuss the influencing factors of WEF system stress and forecast the WEF system stress index (WEF_SI) for 2021-2030. We find that China's WEF_SI has a significant positive spatial autocorrelation, with energy stress being the dominant stress in China's WEF system. Second, GDP per capita, urban population density, education level per capita, technology level and effective irrigated area have spatial and temporal heterogeneity in their effects on WEF system stress. Third, the prediction results show that China's WEF system stress will decrease in 2021-2030 but to a lesser extent. The government should coordinate the relationship between water, energy and food based on the evolutionary characteristics and projected trends of each element and formulate differentiated policies according to the resource endowment of each region to promote the coordinated development of the WEF system.

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.

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.