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

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed

Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m2 at the minimum and 0.57 kg/m2 at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R2 = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO-3-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

Spatial-temporal characteristics and driving factors' contribution and evolution of agricultural non-CO2 greenhouse gas emissions in China: 1995-2021

Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

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.

Prediction model and demonstration of regional agricultural carbon emissions based on Isomap-ACO-ET: a case study of Guangdong Province, China

Scientific analysis of regional agricultural carbon emission prediction models and empirical studies are of great practical significance to the realization of low-carbon agriculture, which can help revitalize and build up ecological and beautiful countryside in China. This paper takes agriculture in Guangdong Province, China, as the research object, and uses the extended STIPAT model to construct an indicator system for the factors influencing agricultural carbon emissions in Guangdong. Based on this system, a combined Isomap-ACO-ET prediction model combing the isometric mapping algorithm (Isomap), ant colony algorithm (ACO) and extreme random tree algorithm (ET) was used to predict agriculture carbon emissions in Guangdong Province under five scenarios. Effective predictions can be made for agricultural carbon emissions in Guangdong Province, which are expected to fluctuate between 11,142,200 tons and 11,386,000 tons in 2030. And compared with other machine learning and neural network models, the Isomap-ACO-ET model has a better prediction performance with an MSE of 0.00018 and an accuracy of 98.7%. To develop low-carbon agriculture in Guangdong Province, we should improve farming methods, reduce the intensity of agrochemical application, strengthen the development and promotion of agricultural energy-saving and emission reduction technologies and low-carbon energy sources, reduce the intensity of carbon emissions from agricultural energy consumption, optimize the agricultural planting structure, and develop green agricultural products and agro-ecological tourism according to local conditions. This will promote the development of agriculture in Guangdong Province in a green and sustainable direction.

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

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

Factor decomposition for ecological pressure of the whole industrial energy carbon footprint: a case study of China

The purpose of this paper is to study the influencing factors of the ecological pressure of the energy carbon footprint (EPECF) of China's whole industry from 2000 to 2018. First, the EPECF of 48 sub industries is calculated, then divides 48 sub-industries into high-, medium-, and low-pressure industries, and uses the logarithmic mean Divisia index (LMDI) method to analyze and summarize the main driving forces of China's industrial EPECF changes. Finally, policy suggestions for the future industrial decompression are put forward. The main results are as follows: (1) Economic development is the most important factor to promote the growth of EPECF of the three major industries. (2) At present, the population pressure factors of forest and grassland have little effect, and the effect of returning farmland to forest and grassland has not been truly played. (3) The adjustment of industrial structure has gradually become a key factor in reducing EPECF of the three industries. (4) The gradual stability of energy intensity has a certain inhibitory effect on the increase of EPECF in high-pressure industry. (5) The adjustment of energy structure in low-pressure industry has gradually worked. Therefore, the government should establish an economic sustainable development system, vigorously develop clean energy, and realize the green transformation of various industries. This provides an empirical example for other countries in the world to reduce the EPECF.

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

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

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.

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China

Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu's ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The "H-H" agglomeration is mainly concentrated in Lianyungang and Suqian, while the "L-L" agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, -0.127%, -0.116%, 0.192%, -0.110%, -0.114%, and -0.123% in Jiangsu's ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.

Exposure and Inequality of PM2.5 Pollution to Chinese Population: A Case Study of 31 Provincial Capital Cities from 2000 to 2016

Fine particulate matter (PM_2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM_2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM_2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM_2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM_2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM_2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m^-3 per year from 2000 to 2016. The proportion of the population exposed to high PM_2.5 (above 35 μg m^-3) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM_2.5.

Does agricultural mechanization improve agricultural environment efficiency? Evidence from China's planting industry

Environmental problems caused by energy consumption in the rapid popularization of China's agricultural mechanization (AM) have caused increasing concern. Using the panel data of China's 30 provinces from 2001 to 2019, this article adopts a stochastic frontier analysis method with output-oriented distance function to measure agricultural environment efficiency (AEE) based on net carbon sinks and empirically analyzes the impact of AM on AEE. The main findings are as follows: Firstly, the AEE of the nation and all provinces shows an upward trend over time and has significant spatial positive autocorrelation characteristics. Secondly, there is an inverted U-shaped relationship between AM and AEE. Meanwhile, AM has spatial spillover effect and time cumulative effect on AEE. These basic conclusions are still robust after using instrumental variables, spatial autoregressive model, sub-sample regression, changing spatial weight matrix, and independent variable. Thirdly, the effect of AM on AEE depends on the input effect and output effect caused by AM. The mechanism is mainly reflected in agricultural technology progress, expansion of the scale of farming operation, optimization of resource allocation, and spatial spillover. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications.

Changes in Energy-Related Carbon Dioxide Emissions of the Agricultural Sector in Poland from 2000 to 2019

This paper analyzes the changes in carbon dioxide (CO2) emissions related to energy consumption in the Polish agricultural sector between 2000 and 2019. Based on the Logarithmic Mean Divisia Index (LMDI), the changes in agricultural CO2 emissions are viewed in the context of changes in six factors, i.e., CO2 emission intensity, substitution of fossil fuels, penetration of renewable energies, energy intensity, labor productivity and number of employees. The analysis demonstrated that total energy consumption declined over the study period; this was related to a reduction in the intake of energy derived from solid fossil fuels (−1.05%), crude oil (−1.01%), electricity (−4.89%), and heat (−1.37%), and to an increased consumption of natural gas (5.78%) and biofuels (0.82%). Furthermore, it follows from the analysis that changes in CO2 emissions witnessed in that period were consistent with changes in energy consumption levels; this resulted from a negligible transformation of the energy mix (largely determined by fossil fuels). Generally, CO2 emissions declined over the study period at a rate comparable (−0.9%) to that of the reduction in energy consumption (−1.03%). In light of the LMDI method, the reduction in CO2 emissions from fuel consumption in the Polish agricultural sector was mainly driven by a reduction in energy intensity and in employment. Conversely, rapid growth in labor productivity was the key factor in increasing carbon dioxide emissions. Compared to these impacts, changes in other factors (i.e., emission intensity, energy mix and penetration of renewable energies) had an extremely small or marginal effect on the variation in CO2 emissions.

Decoupling pollution-agricultural growth and predicting climate change impacts on decoupling index using Bayesian network in different climatic regions

Applying the principles of healthy products through agriculture practices has become an important issue due to significant environmental impacts of agrochemicals application. The agrochemicals have been recognized as an essential component of modern agriculture, but they are also an important source of environmental pollution that threatens the human's health and are main sources of carbon emissions. Pesticides and fertilizers application are important in the process of Iran's food production. In Iran, intensifying the agricultural production has led to overuse of chemical fertilizers and pesticides. This work is the first effort to quantify and compare the decoupling index pollution from agricultural sector using Tapio decoupling indicator and predict climate change impacts on this index by using Bayesian network across the whole country of Iran. For this purpose, required annual data of predictor variables for the period of 2008 to 2018 was used to calculate the decoupling index. For projecting climate change impacts on this index by using Bayesian network, monthly mean values of climatic variables were used. While Iranian farmers are criticized for pesticide overuse, these study findings showed that during the period of 2008-2018, decoupling index for pesticides (agricultural pollution by using pesticide) and decoupling index for fertilizer (agricultural pollution by using fertilizer) in the selected provinces fluctuate between RD-SD, SD-SD, SD-SD, and RD-SD. Therefore, the decoupling states show that in most study years, there is a strong decoupling of agricultural growth in selected provinces. This means that in the selected provinces, pollutant emissions of chemical fertilizer and pesticides use for agricultural productions have decreased and it has been well controlled. Therefore, by expansion of agricultural sector, the situation of agricultural pollution in these provinces in most years has not been intensified. Control of agricultural pollution in these provinces has shown a positive and significant impact on public health. In selected provinces, the cleaner agricultural products and application of organic fertilizers have been increased. This study results also showed that the climate change will accelerate increment of pests population and thus pesticides application in different climatic regions.

Does Agricultural Mechanization Improve the Green Total Factor Productivity of China’s Planting Industry?

Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications.

Prevalence of Carotid Artery Calcification on Digital Panoramic Radiographs in Hemodialysis Patients on Kidney Transplant Waiting List

OBJECTIVES

The detection of carotid artery calcification at an early stage is important to reduce the effects of cardiovascular disease in patients undergoing hemodialysis. This study sought to evaluate the prevalence of carotid artery calcification from panoramic radiographs of patients who were undergoing hemodialysis and to assess the relationship between such calcification and certain medical and periodontal parameters.

MATERIALS AND METHODS

We evaluated 120 panoramic radiographs from patients who were undergoing hemodialysis for the presence of carotid artery calcification. Full-mouth periodontal clinical and medical parameters were recorded, and patients were diagnosed on the basis of the new periodontal disease classification. Patient medical records from the same period (the same week) during which the panoramic radiographs were taken were also assessed.

RESULTS

Among the 120 participating patients, pano - ramic radiographs from 27 patients (22.5%) showed a uni- or bilaterally radiopaque mass. Of the periodontal clinical parameters investigated for associations between patients with and without carotid artery calcification, there was only a significant difference shown for probing pocket depth (P = .017). No significant differences were found between the groups with and without carotid artery calcification with regard to any other medical or periodontal parameter.

CONCLUSIONS

In our study group, suspected carotid artery calcifications were detected on panoramic radiographs in about one-fourth of total patients receiving hemodialysis. Because of the significant relationship found between probing pocket depth and carotid artery calcification, the presence of periodontal disease may be associated with calcifications in these patients. Dentists should maintain awareness in detecting these lesions when evaluating panoramic radiographs of patients undergoing hemodialysis.

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

The current study aims to analyze the regional differences and spatiotemporal dynamic evolution of carbon emission intensity (CEI) and carbon emission per capita (CEPC) of planting industry with consideration of carbon sink effect. The results indicate that: (i) The CEI and CEPC of China's planting industry present significant non-equilibrium distribution characteristic during the investigate period, provinces with high CEI are mainly distributed in major agricultural provinces, while high CEPC provinces are mainly located in northeast and individual central provinces with large planting industry. (ii) Inter-regional difference is the principal course of the total differences, the CEI Theil index demonstrates gradient decreasing pattern of "western > central > eastern > northeast," the contribution rate of CEI Theil index shows "northeast > eastern > central > western," the CEPC Theil index shows the spatial pattern of "northeast > central > western > eastern," and the contribution rate of CEPC Theil index presents the spatial pattern of "eastern > central > western > northeast." (iii) The dynamic evolution of CEI and CEPC curve presents polarization or multipolar differential phenomenon accompanies with distinct gradient characteristics, the regional difference of agglomeration level in CEI is gradually narrowing, while the CEPC gradually expanding and the dispersion level is increasing, which implies the "intra-regional convergence and inter-regional divergence." Consequently, differential carbon reduction policies have been put forward according to the study findings.

Study on Mechanisms Underlying Changes in Agricultural Carbon Emissions: A Case in Jilin Province, China, 1998-2018

Reducing agricultural carbon emissions (ACE) is a key point to achieve green and sustainable development in agriculture. Based on the ACE statistics of Jilin Province in China from 1998 to 2018, this article considers the sources of ACE in depth, and fourteen different carbon sources are selected to calculate ACE. Besides, the paper explores the variation characteristics of ACE in Jilin Province, their structure, and the relationship between the intensity and density of the dynamic changes in ACE in the province in terms of time. Finally, this paper uses the Kaya identity and logarithmic mean Divisia index (LMDI) to analyze the influential factors in ACE. The results show the following: (1) During 1998–2018, the amount of ACE in Jilin Province increased, with an average annual growth rate of 1.13%. However, the chain growth rate has been negative in recent years, which reflects that carbon emission reduction has been achieved to a certain extent. (2) The characteristics of ACE in Jilin Province during the years is that of the low-intensity, high density category. Furthermore, agricultural resource input is the main source of the planting industry’s carbon emissions. From the perspective of animal husbandry, the proportion of CH₄ decreased, while the proportion of N₂O is relatively stable. (3) Based on the LMDI decomposition model, production efficiency, industrial structure, and labor are the three main factors that reduce ACE in Jilin Province. The economic level is the main factor of ACE, and it will be the most important factor leading to an increase in ACE in the short term. On the basis of comprehensive analysis, this article puts forward reasonable suggestions in terms of policy improvement, production mode and industrial structure adjustment, technological innovation, and talent introduction.

The Impact of Planting Industry Structural Changes on Carbon Emissions in the Three Northeast Provinces of China

This paper focuses on the impact of changes in planting industry structure on carbon emissions. Based on the statistical data of the planting industry in three provinces in Northeast China from 1999 to 2018, the study calculated the carbon emissions, carbon absorptions and net carbon sinks of the planting industry by using crop parameter estimation and carbon emissions inventory estimation methods. In addition, the multiple linear regression model and panel data model were used to analyze and test the carbon emissions and net carbon sinks of the planting industry. The results show that: (1). The increase of the planting area of rice, corn, and peanuts in the three northeastern provinces of China will promote carbon emissions, while the increase of the planting area of wheat, sorghum, soybeans, and vegetables will reduce carbon emissions; (2). Fertilizer application, technological progress, and planting structure factors have a significant positive effect on net carbon sinks, among which the changes in the planting industry structure have the greatest impact on net carbon sinks. Based on the comprehensive analysis, it is suggested that, under the guidance of the government, resource endowment and location advantages should be given full play to, and the internal planting structure of crops should be reasonably adjusted so as to promote the development of low-carbon agriculture and accelerate the development process of agricultural modernization.

Carbon Emission Reduction—Carbon Tax, Carbon Trading, and Carbon Offset

The Paris Agreement was signed by 195 nations in December 2015 to strengthen the global response to the threat of climate change following the 1992 United Nations Framework Convention on Climate Change (UNFCC) and the 1997 Kyoto Protocol [...]

Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China

The study of the carbon emission intensity of agricultural production is of great significance for the formulation of a rational agricultural carbon reduction policy. This paper examines the regional differences, spatial–temporal pattern and dynamic evolution of the carbon emission intensity of agriculture production from 1991 to 2018 through the Theil index and spatial data analysis. The results are shown as follows: The overall differences in carbon emission intensity of agriculture production presents a slightly enlarging trend, while the inter-regional differences in carbon emissions intensity is decreasing, but the intra-regional difference of carbon emissions intensity presented an expanding trend. The difference in carbon emission intensity between the eastern and central regions is not obvious, and the difference in carbon emission intensity in the western region shows a fluctuating and increasing trend. The overall differences caused by intra-regional differences; the average annual contribution of intra-regional differences is 67.84%, of which the average annual contribution of western region differences is 64.24%. The carbon emission intensity of agricultural production in China shows a downward trend, with provinces with high carbon emission intensity remaining stable, while provinces with low intensity are expanding. The Global Moran’s I index indicates that China’s carbon emission intensity of agricultural production shows a clear trend of spatial aggregation. The agglomeration trend of high agricultural carbon emission remains stable, and the overall pattern of agricultural carbon emission intensity shows a pattern of increasing differentiation from east to west.

Research on Agricultural Carbon Emissions and Regional Carbon Emissions Reduction Strategies in China

Carbon emissions and strategies for reducing them have become hot topics in recent years. This study firstly measured the total amount and the intensity of agricultural carbon emissions (i.e., agricultural carbon emission per capital) in China. The results show that China’s total carbon emission in 2016 was 272.022 million tons, which is 26.67% more than that in 2000, with an average annual increase of 1.67%. It then compared the regional differences of agricultural carbon emissions in China using the method of coefficient of variation and the Theil index. Following this, this paper finally provides scientific and technological support for the reduction of agricultural carbon emissions in China based on a matrix of carbon emission reduction strategies.

Study of the Spatio-Temporal Differentiation of Factors Influencing Carbon Emission of the Planting Industry in Arid and Vulnerable Areas in Northwest China

Due to the importance of understanding the relationship between agricultural growth and environmental quality, we analyzed how high-quality agricultural development can affect carbon emissions in Northwest China. Based on the concept of the environmental Kuznets curve, this study uses provincial panel data from 1993 to 2017 to make empirical analyses inflection point changes and spatio-temporal differences in agricultural carbon emissions. The highlights of our findings are as follows: (1) In Northwest China, there is an inverse N-shape curve, and the critical values are 3578 yuan/hm2 and 45,738 yuan/hm2, respectively. (2) For 2017, the agricultural economic intensity was 50,670 yuan/hm2, exceeding the critical value (high inflection point) of 45,738 yuan/hm2. (3) Ningxia, Gansu, and Qinghai have not reached the turning point. Having comparable climate, natural conditions, and overall environmental factors, these three provinces would reach the turning point at similar time periods. (4) The average value in agricultural carbon emission intensity in the region is 767.79 kg/hm2, and the order based on intensity is Xinjiang > Shaanxi > Ningxia > Gansu > Qinghai.