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Jin M, Feng Y, Wang S, Chen N, Cao F. Can the development of the rural digital economy reduce agricultural carbon emissions? A spatiotemporal empirical study based on China's provinces. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 939:173437. [PMID: 38796024 DOI: 10.1016/j.scitotenv.2024.173437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/15/2024] [Accepted: 05/20/2024] [Indexed: 05/28/2024]
Abstract
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.
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Affiliation(s)
- Mingming Jin
- School of Economics and Management, Beijing Forestry University, No.35, Tsinghua East Road, Haidian District, Beijing 100083, China.
| | - Yong Feng
- School of Economics and Management, Beijing Forestry University, No.35, Tsinghua East Road, Haidian District, Beijing 100083, China
| | - Shuokai Wang
- School of Economics and Management, Beijing Forestry University, No.35, Tsinghua East Road, Haidian District, Beijing 100083, China
| | - Ni Chen
- School of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Fangping Cao
- School of Economics and Management, Beijing Forestry University, No.35, Tsinghua East Road, Haidian District, Beijing 100083, China.
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Wang J, Chang G, Liu H, Yin Z, Liu P, Zhao Y, Li K, Gao T. Carbon balance analysis of agricultural production systems in oasis areas. Sci Rep 2024; 14:16698. [PMID: 39030311 PMCID: PMC11271539 DOI: 10.1038/s41598-024-66972-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 07/05/2024] [Indexed: 07/21/2024] Open
Abstract
China is the biggest emitter of greenhouse gases (GHGs) in the world, and agricultural GHG emission accounts for nearly a fifth of the total emission in China. To understand the carbon absorption and emission characteristics of agricultural production systems in those arid oasis areas, a typical representative city in northwestern China, Zhangye City, was selected for study.The emission factor method was used to analyze and calculate the characteristics of changing carbon emission dynamics in the whole agricultural production system in Zhangye city region (38,592 km2) from 2010 to 2021.The results revealed that carbon emissions during agricultural planting mainly come from fertilizers, which account for the highest proportion (47.9%) of total carbon emissions in agricultural planting. Animal enteric fermentation emissions from local livestock farming are the main contributor (86%) to GHG emissions. The annual average carbon absorption intensity is 4.4 t C-eq ha-1 for crop and 2.6 t C-eq ha-1 for the agricultural production system. The ratio of total carbon emissions from agricultural production to carbon sequestration of crops is 1:1.7. We find that the total carbon sequestration slightly exceeds its total carbon emissions in the study region, with an annual average of 41% for its sustainable development index. Carbon emissions of the agricultural production system in this oasis area are mainly driven by the livestock industry, mostly CH4 emissions from cattle raising.Reducing the local carbon emissions from the livestock industry, typically the cattle raising, will play a crucial role in reducing carbon emissions from this local agricultural production system and maintaining its net positive carbon balance.
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Affiliation(s)
- Jinxiang Wang
- College of Environment and Urban Construction, Lanzhou City University, The Engineering Research Center of Mining Pollution Treatment and Ecological Restoration of Gansu Province, Lanzhou, 730070, Gansu, China
| | - Guohua Chang
- College of Environment and Urban Construction, Lanzhou City University, The Engineering Research Center of Mining Pollution Treatment and Ecological Restoration of Gansu Province, Lanzhou, 730070, Gansu, China.
| | - Hao Liu
- Pratacultural College, Gansu Agricultural University, Lanzhou, 730070, Gansu, China
| | - Zhuoxin Yin
- College of Environment and Urban Construction, Lanzhou City University, The Engineering Research Center of Mining Pollution Treatment and Ecological Restoration of Gansu Province, Lanzhou, 730070, Gansu, China
| | - Panliang Liu
- College of Environment and Urban Construction, Lanzhou City University, The Engineering Research Center of Mining Pollution Treatment and Ecological Restoration of Gansu Province, Lanzhou, 730070, Gansu, China
| | - Yaling Zhao
- College of Environment and Urban Construction, Lanzhou City University, The Engineering Research Center of Mining Pollution Treatment and Ecological Restoration of Gansu Province, Lanzhou, 730070, Gansu, China
| | - Kaiming Li
- College of Environment and Urban Construction, Lanzhou City University, The Engineering Research Center of Mining Pollution Treatment and Ecological Restoration of Gansu Province, Lanzhou, 730070, Gansu, China
| | - Tianpeng Gao
- College of Environment and Urban Construction, Lanzhou City University, The Engineering Research Center of Mining Pollution Treatment and Ecological Restoration of Gansu Province, Lanzhou, 730070, Gansu, China
- College of Biological and Environmental Engineering, Xi'an University, Xi'an, 710065, China
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Yao Y, Bi X, Li C, Xu X, Jing L, Chen J. A united framework modeling of spatial-temporal characteristics for county-level agricultural carbon emission with an application to Hunan in China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 364:121321. [PMID: 38870785 DOI: 10.1016/j.jenvman.2024.121321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/02/2024] [Accepted: 05/30/2024] [Indexed: 06/15/2024]
Abstract
Effectively tackling extreme climate change requires sound knowledge about carbon emissions and their driving forces. Currently, agricultural carbon emission assessment often deals with its inventory, efficiency, determinants, and response independently, which will leave out the complex interactions among its various components, thus there is a lack of comprehensive, scalable, comparable explanations for agricultural carbon emissions. Herein, we introduce an integrated agricultural carbon emission assessment framework (IEDR): Inventory (I) × Efficiency (E) × Determinants (D) × Response (R), which was then applied to an illustration for the county-level agricultural carbon emissions in Hunan Province, China. Results show that: (1) Agricultural carbon emission inventory (ACEI) increased from 20.06 × 106 tC in 2006 to 21.99 × 106 tC in 2014 and decreased to 19.07 × 106 tC by 2020, depicting a fluctuating trend. Meanwhile, there was remarkable spatial heterogeneity, with higher ACEI in the North and South than in the East and West. (2) Agricultural carbon emission efficiency (ACEE) increased from 0.8520 in 2006 to 0.8992 in 2020, depicting a growing trend driven by technological progress. Spatially distributed in contrast to ACEI, regions with higher ACEE were located in the eastern and western areas. (3) ACEI was negatively correlated with ACEE (-0.657), indicating that increasing ACEE is a key strategy for reducing emissions. (4) The natural environment, rural development level, and policy support had critical impacts on ACEE and ACEI. In particular, the cultivated area and rural water affairs development were significant influences on ACEE and ACEI. Given the externalities of carbon emissions and its important public goods characteristics of the atmosphere, local carbon issues are also global concerns. Therefore, the case study of the IEDR model not only validates this theoretical paradigm and realizes regional responsibility for global carbon reduction but also supports and expands the theoretical and empirical corpus in the field of agricultural carbon emissions and efficiency, providing insights and references for other global regions facing similar challenges.
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Affiliation(s)
- Yao Yao
- Central South University of Forestry and Technology, Changsha, 410004, China.
| | - Xu Bi
- Central South University of Forestry and Technology, Changsha, 410004, China.
| | - Chunhua Li
- Central South University of Forestry and Technology, Changsha, 410004, China.
| | - Xuanhua Xu
- Central South University, Changsha, 410083, China.
| | - Lei Jing
- Central South University of Forestry and Technology, Changsha, 410004, China.
| | - Jiale Chen
- Central South University of Forestry and Technology, Changsha, 410004, China.
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Wu Z, Zheng X, Chen Y, Huang S, Duan C, Hu W. Regional differences and dynamic evolution of high-quality development in service industry: A case study of the Chengdu-Chongqing economic circle. PLoS One 2024; 19:e0297755. [PMID: 38427677 PMCID: PMC10906907 DOI: 10.1371/journal.pone.0297755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 01/12/2024] [Indexed: 03/03/2024] Open
Abstract
The high-quality development of service industry has become an important engine for promoting sustainable economic development. This paper first constructed the evaluation index system of high-quality development of service industry, based on panel data from 2005 to 2020. Second, Kernel density, Markov chain and Dagum Gini coefficient were used to represent the regional differences and dynamic evolution of service industry, and the Koo method was used to explore the characteristics of spatial agglomeration. Finally, social network analysis was used to identify core indicators. The study found that: (1) From 2005 to 2020, the overall level of service industry first decreases and then increases, with Chengdu and Chongqing leading other cities. (2) The development of service industry in the CCEC has large spatial differences, mainly due to inter-regional differences. (3) The level of spatial agglomeration is less variable, with high agglomeration mainly in Chengdu. (4) Indicators such as the level of human capital are the core factors of its high-quality development. This study is of great theoretical and practical significance for the optimization and upgrading of service industry in the CCEC and the synergetic development of the region.
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Affiliation(s)
- Zhixia Wu
- Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang, China
- College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, China
- College of Management, Sichuan University of Science & Engineering, Zigong, China
| | - Xiazhong Zheng
- Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang, China
- College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, China
| | - Yijun Chen
- College of Management, Sichuan University of Science & Engineering, Zigong, China
| | - Shan Huang
- College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Dujiangyan, China
| | - Chenfei Duan
- Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang, China
- College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, China
| | - Wenli Hu
- College of Management, Sichuan University of Science & Engineering, Zigong, China
- College of Business Administration, Rajamangala University of Technology Thanyaburi, Bangkok, Thailand
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Zhang L, Cai C. Innovative measurement, trade-off-synergy relationship and influencing factors for agricultural net carbon emissions and effective supply of agricultural products in China. Heliyon 2024; 10:e24621. [PMID: 38314268 PMCID: PMC10837502 DOI: 10.1016/j.heliyon.2024.e24621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/05/2023] [Accepted: 01/11/2024] [Indexed: 02/06/2024] Open
Abstract
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.
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Affiliation(s)
- Lin Zhang
- Economic Institute, Guizhou University of Finance and Economics, Guiyang, 550025, China
| | - Chengzhi Cai
- Economic Institute, Guizhou University of Finance and Economics, Guiyang, 550025, China
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Qi Y, Liu H, Zhao J, Zhang S, Zhang X, Zhang W, Wang Y, Xu J, Li J, Ding Y. Trends and driving forces of agricultural carbon emissions: A case study of Anhui, China. PLoS One 2024; 19:e0292523. [PMID: 38346018 PMCID: PMC10861070 DOI: 10.1371/journal.pone.0292523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 09/23/2023] [Indexed: 02/15/2024] Open
Abstract
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.
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Affiliation(s)
- Yanwei Qi
- School of Economics & Management, Xidian University, Xi’an, China
| | - Huailiang Liu
- School of Economics & Management, Xidian University, Xi’an, China
| | - Jianbo Zhao
- School of Economics & Management, Xidian University, Xi’an, China
| | - Shanzhuang Zhang
- School of Economics & Management, Xidian University, Xi’an, China
| | - Xiaojin Zhang
- School of Economics & Management, Xidian University, Xi’an, China
| | - Weili Zhang
- School of Economics & Management, Xidian University, Xi’an, China
| | - Yakai Wang
- School of Economics & Management, Xidian University, Xi’an, China
| | - Jiajun Xu
- School of Economics & Management, Xidian University, Xi’an, China
| | - Jie Li
- School of Economics & Management, Xidian University, Xi’an, China
| | - Yulan Ding
- School of Economics & Management, Xidian University, Xi’an, China
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Fu J, Ding R, Zhu YQ, Du LY, Shen SW, Peng LN, Zou J, Hong YX, Liang J, Wang KX, Xiao WQ. 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. ENVIRONMENTAL RESEARCH 2023; 237:116881. [PMID: 37595829 DOI: 10.1016/j.envres.2023.116881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/20/2023] [Accepted: 08/11/2023] [Indexed: 08/20/2023]
Abstract
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.
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Affiliation(s)
- Jun Fu
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China; Guizhou Collaborative Innovation Center of Green Finance and Ecological Environment Protection, Guiyang 550025, China; Artificial Intelligence and Digital Finance Lab, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Rui Ding
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China; Guizhou Collaborative Innovation Center of Green Finance and Ecological Environment Protection, Guiyang 550025, China; Artificial Intelligence and Digital Finance Lab, Guizhou University of Finance and Economics, Guiyang 550025, China.
| | - Yu-Qi Zhu
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China; Guizhou Collaborative Innovation Center of Green Finance and Ecological Environment Protection, Guiyang 550025, China
| | - Lin-Yu Du
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China; Guizhou Collaborative Innovation Center of Green Finance and Ecological Environment Protection, Guiyang 550025, China
| | - Si-Wei Shen
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China; Guizhou Collaborative Innovation Center of Green Finance and Ecological Environment Protection, Guiyang 550025, China
| | - Li-Na Peng
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Jian Zou
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Yu-Xuan Hong
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Juan Liang
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Ke-Xin Wang
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Wen-Qian Xiao
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
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Sun X, He J, Li C, Chen Y, Li R, Wang Z, Wu W, Li Y, Guo X, Wang X. Field investigation of non-uniform environment in a Venlo-type greenhouse in Yangling, China. Heliyon 2023; 9:e22143. [PMID: 38034636 PMCID: PMC10685296 DOI: 10.1016/j.heliyon.2023.e22143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 10/20/2023] [Accepted: 11/05/2023] [Indexed: 12/02/2023] Open
Abstract
Non-uniform environmental conditioning has established substantial energy-saving and conditioning effects in residential buildings, however, few studies on the technology applied in greenhouses have been conducted. Semi-enclosed greenhouse development is hindered by energy consumption. To better apply non-uniform environmental conditioning technology in greenhouses, it is necessary to investigate the non-uniform characteristics of field environment parameters. Therefore, spatial and temporal measurements of indoor temperature and relative humidity in a Venlo-type greenhouse in Yangling, China, were conducted on June 5-11, 2022. Temperature and humidity sensors were arranged in the greenhouse at 4.5 m intervals, in the canopy, cultivation, center, and root areas. Temperature and humidity measurement points on the greenhouse walls were selected. The measurement results showed large fluctuations in the indoor temperature and relative humidity over time. The difference between indoor and outdoor average temperatures ranged from -5-10 °C and temperatures unsuitable for tomato growth were identified, although some passive conditioning methods such as ventilation and water spraying were employed, which indicates the necessity of active heating and cooling. Based on the measured data, the nonuniformity coefficients of temperature and relative humidity in different directions in the greenhouse were calculated. A larger non-uniformity in the vertical direction was found compared to that in the horizontal direction. These results suggest the possibility of non-uniform environmental conditioning. A rough estimation of the energy consumption by the two different condition modes, namely zone-specific and overall conditioning, was made. A huge energy saving of 69.6 % by the zone-specific conditioning mode was revealed compared to the overall conditioning. This implies a huge advantage in energy efficiency by non-unform environmental conditioning technologies applied in greenhouses. The study provides useful data for understanding non-uniform environments in greenhouses and the application of non-uniform environmental conditioning technologies.
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Affiliation(s)
- Xianpeng Sun
- College of Horticulture, North West Agriculture and Forestry University, Yangling 712100, Shaanxi Province, China
- Key Laboratory of Horticultural Engineering in Northwest Facilities, Ministry of Agriculture, Yangling 712100, Shaanxi Province, China
- Facility Agriculture Engineering Technology Research Center of Shaanxi Province, Yangling 712100, Shaanxi Province, China
| | - Jinhong He
- College of Horticulture, North West Agriculture and Forestry University, Yangling 712100, Shaanxi Province, China
| | - Chuanzhen Li
- College of Horticulture, North West Agriculture and Forestry University, Yangling 712100, Shaanxi Province, China
| | - Yangda Chen
- College of Horticulture, North West Agriculture and Forestry University, Yangling 712100, Shaanxi Province, China
| | - Runjie Li
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi Province, China
| | - Ziteng Wang
- College of Horticulture, North West Agriculture and Forestry University, Yangling 712100, Shaanxi Province, China
| | - Weijun Wu
- College of Horticulture, North West Agriculture and Forestry University, Yangling 712100, Shaanxi Province, China
| | - Yapeng Li
- College of Horticulture, North West Agriculture and Forestry University, Yangling 712100, Shaanxi Province, China
| | - Xuxin Guo
- College of Horticulture, North West Agriculture and Forestry University, Yangling 712100, Shaanxi Province, China
| | - Xinke Wang
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi Province, China
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9
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Qi Y, Liu H, Zhao J. Prediction model and demonstration of regional agricultural carbon emissions based on Isomap-ACO-ET: a case study of Guangdong Province, China. Sci Rep 2023; 13:12688. [PMID: 37542116 PMCID: PMC10403573 DOI: 10.1038/s41598-023-39996-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 08/03/2023] [Indexed: 08/06/2023] Open
Abstract
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.
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Affiliation(s)
- Yanwei Qi
- School of Economics and Management, Xidian University, Xi'an, 710071, China.
| | - Huailiang Liu
- School of Economics and Management, Xidian University, Xi'an, 710071, China
| | - Jianbo Zhao
- School of Economics and Management, Xidian University, Xi'an, 710071, China
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10
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Huang J, Sun Z, Du M. Spatiotemporal characteristics and determinants of agricultural carbon offset rate in China based on the geographic detector. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:58142-58155. [PMID: 36977880 DOI: 10.1007/s11356-023-26659-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 03/22/2023] [Indexed: 05/10/2023]
Abstract
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.
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Affiliation(s)
- Jie Huang
- Business School, Xinyang Normal University, Xinyang, 464000, Henan, China
| | - Zimin Sun
- Business School, Xinyang Normal University, Xinyang, 464000, Henan, China
| | - Minzhe Du
- School of Economics and Management, South China Normal University, Guangzhou, 510006, Guangdong, China.
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11
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Xinxing S, Sarkar A, Yue D, Hongbin Z, Fangyuan T. The influences of the advancement of green technology on agricultural CO2 release reduction: A case of Chinese agricultural industry. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2023. [DOI: 10.3389/fsufs.2023.1096381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
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.
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12
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Study on carbon emission reduction effect of institutional openness in China. Sci Rep 2023; 13:254. [PMID: 36604486 DOI: 10.1038/s41598-023-27442-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 01/02/2023] [Indexed: 01/07/2023] Open
Abstract
As the main means to dovetail the domestic system with international rules, institutional openness is the key to deepening participation in the global economic governance system, breaking through energy and carbon emission constraints, and achieving green and sustainable economic development. Taking 284 prefecture-level cities in China from 2006 to 2019 as the research sample, this paper uses the establishment of Pilot Free Trade Zones as a quasi-natural experiment to systematically identify and test the actual impact of institutional openness on urban carbon emissions in China through the asymptotic difference in difference method, instrumental variables method, spatial econometric model, and mediating effects model. Meanwhile, technological progress is used as the entry point to analyze the intrinsic mechanism of action by adopting digital transformation oriented to efficiency improvement and green innovation capability oriented to R&D innovation as the differentiated perspective. It is found that institutional openness significantly suppresses urban CO2 emissions, and there is a certain heterogeneity and spatial spillover effect of this effect. Further study finds that institutional openness achieves carbon emission reduction through technological progress. The study aims to find new institutional innovation and development paths for low carbon development.
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Liu Y, Ye D, Liu S, Lan H. The effect of China's leading officials' accountability audit of natural resources policy on provincial agricultural carbon intensities: the mediating role of technological progress. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:5634-5661. [PMID: 35980529 DOI: 10.1007/s11356-022-22465-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
China is one of the largest agricultural countries in the world. In the context of China's efforts to achieve dual carbon goals (carbon peak and carbon neutral), the need for carbon emissions reductions in the agricultural sector cannot be ignored. This study collected 2007 to 2018 data from 30 Chinese provinces and used a difference in differences (DID) model to investigate the relationships between China's leading officials' accountability audit of natural resources policy (LOAANR), agricultural technological progress, and agricultural carbon emissions intensities (CEI). A common trend test, placebo test, PSM-DID, and other test methods were used to verify the reliability of the results. The results show that (1) compared with the non-pilot areas, the policy implementation could significantly reduce CEI; (2) the LOAANR was able to stimulate patented technological progress (ATI) and mechanical technological progress (AMT), but different types of technological progress had different mediation effect sizes; and (3) the policy effects shows obvious regional heterogeneity, manifesting as west > east > central; and the policy effects were more obvious in the non-major grain-producing areas, but had no inhibition effects on the CEI in the major grain-producing areas; compared with low intensity market-based environmental regulation (MER) regions, high-intensity MER regions have stronger LOAANR emission reduction effects. Based on the study findings, policy suggestions are given to reduce agricultural carbon emissions and promote higher quality agricultural development.
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Affiliation(s)
- Yunqiang Liu
- College of Management, Sichuan Agricultural University, 211 Huimin Road, Wenjiang District, Chengdu, 611130, China
| | - Deping Ye
- College of Management, Sichuan Agricultural University, 211 Huimin Road, Wenjiang District, Chengdu, 611130, China
| | - Sha Liu
- College of Management, Sichuan Agricultural University, 211 Huimin Road, Wenjiang District, Chengdu, 611130, China
| | - Hongxing Lan
- College of Management, Sichuan Agricultural University, 211 Huimin Road, Wenjiang District, Chengdu, 611130, China.
- Sichuan Center for Rural Development Research, Chengdu, 611130, China.
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14
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Wu H, MacDonald GK, Galloway JN, Geng Y, Liu X, Zhang L, Jiang S. A new dietary guideline balancing sustainability and nutrition for China's rural and urban residents. iScience 2022; 25:105048. [PMID: 36185362 PMCID: PMC9519510 DOI: 10.1016/j.isci.2022.105048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/31/2022] [Accepted: 08/26/2022] [Indexed: 11/21/2022] Open
Abstract
Diets have important but often complex implications for both environmental quality and nutrition. We establish a production-oriented life cycle model to quantify and compare the farm-to-gate environmental impacts and food nutritional qualities underlying rural and urban diets in China from 1980 to 2019, a period of rapid urbanization and socioeconomic changes. The environmental impacts of rural diets were generally higher than those of urban diets, but this gap reduced after 2000. Environmental and nutritional values varied considerably across the 31 Chinese provinces due to their different food intakes and dietary structures. Dietary changes coinciding with urbanization increased greenhouse gas emissions, eutrophication potential, and nutritional quality, but decreased energy consumption and acidification potential. Based on our results, we propose a new dietary guideline to mitigate environmental impacts and improve nutritional quality.
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Affiliation(s)
- Huijun Wu
- School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
| | | | - James N. Galloway
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22904, USA
| | - Yong Geng
- School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200240, China
- China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai 200030, China
- School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China
| | - Xin Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, P.R. China
| | - Ling Zhang
- College of Economics and Management, Nanjing Forestry University, Nanjing 210037, China
| | - Songyan Jiang
- School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
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15
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Zhu Y, Zhang Y, Piao H. Does agricultural mechanization improve agricultural environment efficiency? Evidence from China's planting industry. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:53673-53690. [PMID: 35290580 DOI: 10.1007/s11356-022-19642-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Yingyu Zhu
- College of Economics and Management, Shenyang Agricultural University, Shenyang, 110866, China
| | - Yan Zhang
- College of Economics and Management, Shenyang Agricultural University, Shenyang, 110866, China.
- Institute of Higher Education, Shenyang Agricultural University, Shenyang, 110866, China.
| | - Huilan Piao
- College of Economics and Management, Shenyang Agricultural University, Shenyang, 110866, China
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16
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Zhou X, Yu J, Li J, Li S, Zhang D, Wu D, Pan S, Chen W. Spatial correlation among cultivated land intensive use and carbon emission efficiency: A case study in the Yellow River Basin, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:43341-43360. [PMID: 35094255 DOI: 10.1007/s11356-022-18908-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/23/2022] [Indexed: 06/14/2023]
Abstract
Considering the current global goal of carbon neutrality, the relationship between cultivated land intensive use (CLIU) and carbon emission efficiency (CEE) should be explored to address the global climate crisis and move toward a low-carbon future. However, previous work in this has been conducted at provincial/regional scales and few have identified the spatial correlation between CLIU and CEE at the scale of large river basins. Therefore, this study explored the spatiotemporal characteristics of CLIU, cultivated land carbon emissions (CLCE), and CEE, as well as the spatial correlation between CLIU and CEE in the Yellow River Basin (YRB), China. A comprehensive evaluation model, the Intergovernmental Panel on Climate Change (IPCC) coefficient methodology, existing data envelopment analysis model, and bivariate spatial autocorrelation models were used to analyze statistical data from 2005 to 2017. We found that the overall CLIU and CLCE values in the YRB exhibited a continuous increase; the average carbon emission total efficiency and carbon emission scale efficiency first decreased and then increased, and the average carbon emission pure technical efficiency gradually decreased. Areas of high CLCE were concentrated in eastern areas of the YRB, whereas those of high CLIU, carbon emission total efficiency, carbon emission scale efficiency, and carbon emission pure technical efficiency predominantly appeared in the eastern areas, followed by central and western areas of the YRB. Spatial analysis revealed a significant spatial dependence of CLIU on CEE. From a global perspective, the spatial correlations between CLIU and CEE changed from positive to negative with time. Moreover, the aggregation degree between CLIU and CEE gradually decreases with time, while the dispersion degree increases with time, and the spatial correlation gradually weakens. The local spatial autocorrelation further demonstrates that the number of high-low and low-high clusters between CLIU and CEE gradually increases over time, while the number of high-high and low-low clusters gradually decreased over time. Collectively, these findings can help policymakers formulate feasible low-carbon and efficient CLIU policies to promote win-win cooperation among regions.
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Affiliation(s)
- Xiao Zhou
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Juan Yu
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Jiangfeng Li
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Shicheng Li
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Dou Zhang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Di Wu
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Sipei Pan
- Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China
- Key Laboratory of Rule of Law Research, Ministry of Natural Resources, Wuhan, 430074, China
| | - Wanxu Chen
- Department of Geography, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.
- Research Center for Spatial Planning and Human-Environmental System Simulation, China University of Geosciences, Wuhan, 430074, China.
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China.
- School of Geography and Information Engineering, East Lake New Technology Development Zone, China University of Geosciences, No. 68, Jincheng Street, Wuhan, Hubei Province, 430078, People's Republic of China.
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17
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Digital Economy, Agricultural Technological Progress, and Agricultural Carbon Intensity: Evidence from China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116488. [PMID: 35682072 PMCID: PMC9180528 DOI: 10.3390/ijerph19116488] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 12/10/2022]
Abstract
China is the largest carbon emitter in the world, with agricultural carbon emissions accounting for 17% of China’s total carbon emissions. Agricultural carbon emission reduction has become the key to achieving the “Double Carbon” goal. At the same time, the role of the digital economy in achieving the “dual carbon” goal cannot be ignored as an important engine to boost the high-quality development of China’s economy. Therefore, this paper uses the panel data of 30 provinces in mainland China from 2011 to 2019 to construct a spatial Durbin model and a mediation effect model to explore the impact of the digital economy on agricultural carbon intensity and the mediating role of agricultural technological progress. The research results show that: (1) China’s agricultural carbon intensity fluctuated and declined during the study period, but the current agricultural carbon intensity is still at a high level; (2) The inhibitory effect of the digital economy on agricultural carbon intensity is achieved by promoting agricultural technological progress, and the intermediary role of agricultural technological progress has been verified; (3) The digital economy can significantly reduce the carbon intensity of agriculture, and this inhibition has a positive spatial spillover effect. According to the research conclusions, the government should speed up the development of internet technology and digital inclusive finance, support agricultural technology research and improve farmers’ human capital, and strengthen regional cooperation to release the contribution of digital economy space.
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18
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Influencing Factors and Path Analysis of Sustainable Agricultural Mechanization: Econometric Evidence from Hubei, China. SUSTAINABILITY 2022. [DOI: 10.3390/su14084518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The importance of supporting agricultural mechanization in agri-food supply chains to achieve agricultural and rural development has been comprehensively recognized. There has been a surge in the attention given to Sustainable Agricultural Mechanization (SAM) in the context of developing countries. However, it is important to address the major challenge of studying the important factors and the influencing path of SAM. As a representative province of China’s agricultural development, Hubei has developed significantly in terms of agricultural mechanization in the past 20 years. Therefore, using a literature review, representative field survey data, and statistical analytical approaches, 28 relevant factors related to SAM were extracted, and the main influencing factors of SAM were determined by building an integrative conceptual framework and using the corresponding structural equation model based on partial least squares (PLS-SEM). The relationships and influencing paths between the factors were analyzed, and a confirmatory measurement model and a structural model of the effects on sustainable agricultural mechanization were constructed. The results show that (1) the PLS-SEM model fits the experimental data well and can effectively reflect the relationships among factors in this complex system; (2) within the factors influencing the development level of SAM in Hubei, China, the economic factors have the greatest weight, whereas government policy factors are the core elements promoting development, and environmental factors are the most noteworthy outcome factors; and (3) economic and policy factors play a very obvious role in promoting SAM through the influencing paths of agricultural production and agricultural machinery production and sales. Ultimately, corresponding suggestions have been put forward for decisions regarding the implementation of SAM for similar countries and regions.
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19
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Cheng J, Zou R, Wang H, Geng Z. How does China's industrial wastewater shadow price evolve? The perspective of spatiotemporal characteristics and differences decomposition. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:30363-30382. [PMID: 34997930 DOI: 10.1007/s11356-021-17942-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/01/2021] [Indexed: 06/14/2023]
Abstract
To provide a reference for reducing the cost of industrial wastewater treatment and alleviate the pressure on water environment governance in China, we use the non-parametric dual evaluation linear analysis framework to estimate the shadow price of China's urban industrial wastewater (IWSP) with consideration of multiple inputs based on the data of 267 cities in China from 2003 to 2016. Then, we investigate the spatiotemporal characteristics of IWSP and analyze its sources of differences. Main conclusions are as follows: (1) Mean of China's urban IWSP increased from 645.54 yuan/ton in 2003 to 5662.64 yuan/ton in 2016, implicating the significant results and increasing difficulty of emission reduction policies. In addition, the Moran's I index of IWSP decreased from 0.056 to 0.002, implicating declining spatial correlation and differentiated green production processes in various regions. (2) From stock perspective, the σ convergence result shows that the IWSP of the country and each region gradually diverges, and the β convergence results from incremental perspective show that the IWSP of a single region tends to converge in a steady state. Furthermore, regions with lower average shadow prices converge faster than regions with higher average shadow prices. (3) Using the Dagum Gini coefficient method, we find that the overall difference of IWSP dropped from 0.5758 to 0.3568. The intra-regional differences in each region continued to decline, as well as inter-regional differences. And the contribution rate of intensity of transvariation has risen from 33.71 to 60.80%, becoming the main reason for the imbalanced distribution of IWSP.
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Affiliation(s)
- Jixin Cheng
- School of Economics and Business Administration, Chongqing University, Chongqing, China
| | - Ran Zou
- School of Economics and Business Administration, Chongqing University, Chongqing, China.
| | - Hongxuan Wang
- School of Economics and Business Administration, Chongqing University, Chongqing, China
| | - Zhifei Geng
- Business School, Ningbo University, City, Ningbo, China
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20
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Shi R, Irfan M, Liu G, Yang X, Su X. Analysis of the Impact of Livestock Structure on Carbon Emissions of Animal Husbandry: A Sustainable Way to Improving Public Health and Green Environment. Front Public Health 2022; 10:835210. [PMID: 35223746 PMCID: PMC8873578 DOI: 10.3389/fpubh.2022.835210] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 01/17/2022] [Indexed: 12/28/2022] Open
Abstract
Carbon emissions of animal husbandry have been gaining increasing attention due to their high share in global carbon emissions. In this regard, it is essential to assess the regional differences, dynamic evolution patterns, convergence characteristics, and the impact of livestock structure on carbon emissions of animal husbandry. Using data from 30 provincial administrative regions from 2000 to 2018 in China, this study employs the Thiel index method, kernel density analysis, and convergence analysis to quantify the impact of livestock structure on carbon emissions of animal husbandry. The statistical results reveal that carbon emissions of animal husbandry exhibit a rising and declining trend. Specifically, the carbon emissions of animal husbandry are highest in agricultural areas (with a declining trend), followed by agro-pastoral areas (with a declining trend), and the pastoral areas (with a rising trend). It is further revealed that there are no δ convergence and β convergence of carbon emissions of animal husbandry. Finally, essential and useful policy recommendations are put forward to inhibit carbon emissions of animal husbandry.
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Affiliation(s)
- Rubiao Shi
- School of Marxism, Xinjiang University, Urumqi, China
- Department of Public Instruction, Shandong College of Traditional Chinese Medicine, Yantai, China
| | - Muhammad Irfan
- School of Management and Economics, Beijing Institute of Technology, Beijing, China
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China
- Department of Business Administration, Ilma University, Karachi, Pakistan
- *Correspondence: Muhammad Irfan
| | - Guangliang Liu
- School of Economics and Management, Xinjiang University, Urumqi, China
- Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi, China
| | - Xiaodong Yang
- School of Economics and Management, Xinjiang University, Urumqi, China
- Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi, China
| | - Xufeng Su
- School of Economics and Management, Xinjiang University, Urumqi, China
- Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi, China
- School of Economics and Management, Tarim University, Alar, China
- Xufeng Su
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21
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Spatiotemporal Heterogeneity of Agricultural Land Eco-Efficiency: A Case Study of 128 Cities in the Yangtze River Basin. WATER 2022. [DOI: 10.3390/w14030422] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Analysis of spatiotemporal heterogeneity and evolutionary characteristics of agricultural land eco-efficiency is of great significance for achieving a rational use of natural resources and coordinated development of the agricultural economy as well as the ecological environment. In this study, we construct the “ecological space–agricultural production–carbon emission” framework, incorporate carbon emission intensity as an undesired output into the evaluation index system of agricultural land eco-efficiency, calculate the eco-efficiency of agricultural land in 128 cities in the Yangtze River basin from 2009 to 2018 by adopting the super-efficiency SBM model, and discuss the spatial and temporal changes using methodology such as hotspot analysis and kernel density estimation by ArcGIS. The results show the following. The overall trend of agricultural land eco-efficiency in the Yangtze River basin is increasing year by year and still has potential for improvement. However, there are significant discrepancies among cities, with the eco-efficiency of the downstream being much higher than that of the midstream and upstream regions, and demonstrating the pattern of “big dispersion–small agglomeration”. Some cities are still facing pressure to improve the eco-efficiency of agricultural land. Correspondingly, this paper puts forward optimization recommendations: Firstly, the downstream cities should give full play to their geographical advantages, actively introduce advanced production technologies, and reasonably allocate agricultural resources. Secondly, the upstream and midstream regions should formulate reasonable regional strategies in accordance with their natural resource endowments to improve the ecological benefits of agricultural land and narrow the regional disparities. This paper gives targeted policy recommendations at the levels of paying attention to education of farmers, providing incentives for ecological planting, strengthening agricultural infrastructure construction, reasonably controlling the use of agricultural materials, and increasing investment in agricultural pollutant emission management.
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22
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Does Agricultural Mechanization Improve the Green Total Factor Productivity of China’s Planting Industry? ENERGIES 2022. [DOI: 10.3390/en15030940] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
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.
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23
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Spatial-Temporal Pattern and Evolution Trend of the Cultivated Land Use Eco-Efficiency in the National Pilot Zone for Ecological Conservation in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 19:ijerph19010111. [PMID: 35010371 PMCID: PMC8750054 DOI: 10.3390/ijerph19010111] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/17/2021] [Accepted: 12/21/2021] [Indexed: 11/17/2022]
Abstract
The cultivated land use eco-efficiency (CLUE) is an important indicator to evaluate ecological civilization construction in China. Research on the spatial-temporal pattern and evolution trend of the CLUE can help to assess the level of ecological civilization construction and reveal associated demonstration and driving effects on surrounding areas. Based on the perspective of the CLUE, this paper obtains cultivated land use data pertaining to National Pilot Zones for Ecological Conservation in China and neighboring provinces from 2008 to 2018. In this study, the SBM-undesirable, Moran's I, and Markov chain models are adopted to quantitatively measure and analyze the CLUE and its temporal and spatial patterns and evolution trend. The research results indicate that the CLUE in the whole study area exhibited the characteristics of one growth, two stable, and two decline stages, with a positive spatial autocorrelation that increased year by year, and a spatial spillover effect was observed. Geographical spatial patterns and spatial spillover effects played a major role in the evolution of the CLUE, and there occurred a higher probability of improvement in the vicinity of cities with high CLUE values. In the future, practical construction experience should be disseminated at the provincial level, and policies and measures should be formulated according to local conditions. In addition, a linkage model between prefecture-level cities should be developed at the municipal level to fully manifest the positive spatial spillover effect. Moreover, we should thoroughly evaluate the risk associated with CLUE transition from high to low levels and establish a low-level early warning mechanism.
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