Introduction
Over the course of more than four decades since the initiation of China’s reform and opening-up policy, this nation, with a mere 9% of the world’s arable land and 6% of its freshwater resources, has managed to sustain a staggering 18% of the global population. While making commendable contributions towards achieving sustainable development, China has also borne significant costs in terms of its resource and environmental impact. According to the data presented in the “Second National Pollution Source Census Bulletin,” it is revealed that in 2014, agricultural activities in China accounted for 7.5% of the total greenhouse gas emissions. Furthermore, in 2017, nitrogen and phosphorus runoff from agricultural activities constituted 24% of each of the nationwide total runoff [1]. From a global perspective, it is noteworthy that agriculture production contributes to nearly a quarter of carbon emissions, with its carbon footprint exceeding one-third of the total emissions [2, 3]. Furthermore, the World Bank, in its “World Development Report 2019,” highlights the significant potential of agricultural and forestry production technologies in mitigating carbon emissions, estimated to range from 2.3 to 9.6 billion tons annually. This underscores the dual nature of agriculture as both a significant contributor to carbon emissions and a domain with immense potential for emission reduction [4].
Numerous scholarly works have delved into the intricate interplay between agriculture, economy, and society. Tomal et al. (2021) conducted a spatiotemporal analysis on the coupling coordination and convergence behavior of ecological environment and urban economic development based on data spanning from 2003 to 2019 in various cities in Poland. The findings revealed a relatively low level of coupling coordination between the rural-urban ecological environment and socioeconomic factors in Poland. This can primarily be attributed to the inadequacy of infrastructure and environmental conditions in rural areas. Furthermore, the level of economic and social development in a region can significantly impact its facilities construction [5]. Luo et al. (2021) employed the Backpropagation Artificial Neural Network (BPANN) to analyze the influential factors of coupling coordination among the economic, social, and environmental dimensions in the Yangtze River Delta urban agglomeration. The findings demonstrated that economic growth serves as a foundation for the sustainable development of society and the environment. Furthermore, factors such as urbanization rate and industrial structure exert considerable influence on the level of coupling coordination in the economic, social, and environmental realms [6]. Feng et al. (2021) constructed an evaluation system for the coupling coordination among the agricultural economy, ecology, and society using data from Shaanxi Province spanning from 2006 to 2017. The results indicate that achieving a sustainable and prosperous agricultural sector necessitates a balance between resource supply and demand output. Additionally, the level of socioeconomic development in a region serves as a driving force for the coordinated development of the agricultural economy, ecology, and society [7]. Meanwhile, Tian et al. (2021) examined the coupling coordination and evolutionary characteristics of agricultural carbon emissions and economic growth in the Yangtze River Economic Belt. The findings reveal a gradual increase in the level of coupling coordination between agricultural carbon emissions and economic growth [8].
Green and low-carbon agriculture represents an advanced stage of agricultural development, with its level and capacity relying on the systemic coordination of regional development, particularly the support from the social and economic systems. As illustrated in Fig. 1, the development of green and low-carbon agriculture will enhance the supply of high-quality agricultural products, increase farmers’ income, and indirectly stimulate economic growth. Simultaneously, as economic growth inevitably leads to an increase in residents’ income, it will also enhance their consumption capacity, expand the market demand for high-quality agricultural products, and drive the upgrading and development of agriculture toward a green and low-carbon direction. In response, this will also lead to increased investment in agricultural technology research and development, as well as the enhancement of farmers’ cultural proficiency, in order to meet people’s pursuit of a socially prosperous development. As Hu et al. (2020) argue, economic growth can guide the formation of new industries and promote the transformation and upgrading of industrial structures [9]. Social stability serves as the foundation for economic growth, and the development of science and technology, along with the participation of scientific and technological talents, are variable factors that drive economic growth. Conversely, economic growth also reciprocates with social development. When the economy is in a downturn, the pace of social progress is inevitably affected.
Fig. 1.
Diagram depicting the Mechanism of Green Low-Carbon Agriculture and its Interactions with Socio-Economic Factors.
As agriculture transitions towards green and low-carbon practices, it necessitates the support of relevant industries in terms of technological advancements and skilled personnel. Additionally, the strong demand from society for environmentally-friendly, high-quality agricultural products serves as a positive driving force. Ultimately, these factors combine to create an intrinsic momentum that promotes the development of green and low-carbon agriculture. Therefore, green and low-carbon agriculture is intricately coupled with the social and economic systems, underscoring the vital importance of conducting research in this area for the transformation and development of agriculture towards sustainability. (Fig. 1)
Material and Methods
Indicator Selection and Data Sources
The establishment of a scientific and rational evaluation indicator system is a prerequisite for accurately grasping the level of coupling and coordination of the “green and low-carbon agriculture and socio-economic” system. Based on the aforementioned analysis of the mechanisms underlying the coupling and coordination of the green and low-carbon agriculture and socio-economic system, and guided by principles such as feasibility, authenticity, and hierarchy, a comprehensive evaluation indicator system for the coordinated development of the “green and low-carbon agriculture and socio-economic” system has been constructed [10-13] (Table 1).
Table 1.
Evaluation indicators system for the coordinated development of green and low-carbon agriculture and socio-economic integration.
The primary sources of data for this article are the “China Statistical Yearbook,” “China Rural Statistical Yearbook,” “China Environmental Statistical Yearbook,” “China Forestry and Grassland Statistical Yearbook” from the years 2014-2022, as well as relevant government-published data and provincial (municipal) statistical yearbooks. In cases where specific data was missing, interpolation was conducted using neighboring year values.
Research Methods
The entropy method is used to measure the comprehensive development index of green and low-carbon agriculture
Step 1: Standardization
In this step, the original value of the j th indicator in the i th year is denoted as
For negatively oriented indicators:
Step 2: Calculate the weights of each indicator and determine the information entropy of the j th indicator:
In this step,
Determine the redundancy of information entropy and its weight: In this step,
Step 3: Calculate the comprehensive development indices of each system. In this step,
Table 2.
Agricultural Carbon Emission Calculation Form. Table 2. Agricultural Carbon Emission Calculation Form.
1. Coupling Coordination Degree Model
The formula for calculating the coupling degree is as follows, where C represents the coupling degree,
The calculation of the coupling coordination degree is as follows. D represents the coupling coordination degree,
2. Geographic Weighted Regression Analysis Method
The Geographic Weighted Regression (GWR) analysis method is used to explore the driving factors of green low-carbon agriculture development [16, 17]. Where
Table 3.
Classification and Types of Coupling Degree and Coupling Coordination Level.
Results and Discussion
A Spatio-Temporal Analysis of Coupling and Coordination
The development level of green and low-carbon agriculture (0.0938-0.4158) exhibits a “U-shaped” pattern, initially declining and then rising before stabilizing. Moreover, the development of green and low-carbon agriculture significantly surpasses that of socioeconomic development, indicating its superiority over economic and social progress. The level of socioeconomic development (0.0058-0.1862) shows a consistent annual increase, while the economic development level (0.0684-0.1754) experiences fluctuating growth. Since 2012, the level of socioeconomic development has surpassed economic development, marking China’s departure from the era of “high growth.” This can be attributed to the overall global economic downturn, as well as the domestic impact of slowed investment, foreign trade, and consumption, leading to a sustained deceleration of China’s economic growth. The initial decline in the development of green and low-carbon agriculture reflects the inhibitory effect of earlier extensive economic development and an industrial-oriented economic structure. However, the subsequent introduction of ecological civilization construction and the advancement of green and low-carbon technologies have significantly improved the development of green and low-carbon agriculture compared to the earlier period. (Fig. 2)
Fig. 2.
Indices of Comprehensive Evaluation for National Green Low-Carbon Agriculture and Socio-Economic Systems from 2003 to 2021.
Fig. 3.
Coupling Degree and Coupling Coordination of National Green Low-Carbon Agriculture and Socio-Economic Systems from 2003 to 2021.
The overall coupling between green low-carbon agriculture and socio-economic development in the country has shown a pattern of initial opposition, followed by a period of adjustment, leading to a high-level coupling phase that has been sustained. However, the degree of coordination has gone through three stages: “moderate imbalance, mild imbalance, and approaching imbalance,” indicating that a well-coordinated development situation has yet to be achieved, leaving ample room for improvement (Fig. 3).
According to calculations, it has been found that since 2009, the coupling level of all 31 provinces (municipalities, autonomous regions) in the country has reached a high-level coupling phase. From 2003 to 2009, most regions north of the Yellow River were in an opposition stage, while regions south of the Yellow River were mostly in an adjustment stage. However, there has been significant spatial differentiation in the coupling coordination level across the country from 2003 to 2021, as shown in Fig. 4. The proportion of transitional coordination regions has increased, and the types of coordination levels in different regions have varied over time: “moderate imbalance to mild imbalance” (in 2006, 2011 [* taking into account the corresponding policy planning and influences during the 11th Five-Year Plan (2006-2010), 12th Five-Year Plan (2011-2015), 13th Five-Year Plan (2016-2020), and 14th Five-Year Plan (2021-2025) periods, the chosen time nodes are 2006, 2011, 2016, and 2021 (same for subsequent mentions).]), “mild imbalance to approaching imbalance” (in 2016), and “approaching imbalance to precarious coordination” (in 2021*). Overall, the coordination level has been gradually improving over the observed trend.
In 2006, approximately two-thirds of the regions nationwide exhibited a moderate imbalance in the coupling coordination between green low-carbon agriculture and socio-economic development, while one-third showed a mild imbalance. Spatially, the coordination level was higher in the northern regions and lower in the southern regions. In 2011, the coordination level among 29 provinces (municipalities, autonomous regions) nationwide reached a state of mild imbalance, with only Sichuan and Guizhou remaining in a state of moderate imbalance. In 2016, approximately 42% of the regions nationwide experienced a moderate imbalance, while the remaining areas approached a state of imminent imbalance in terms of the development level of green low-carbon agriculture. The overall pattern mirrored that of “medium-low-high,” with the central regions exhibiting a “south-high-north-low” and an “east-high-west-low” distribution. By 2021, half of the regions achieved a precarious level of coordination, while the other half approached a state of imbalance. In that year, the development level of green low-carbon agriculture in the northern regions was notably lower than that in the southern regions. The southeastern areas had a relatively higher level of development, surpassing the northwestern regions. The spatial characteristics of coupling coordination were also consistent with this pattern. Since 2016, adjustments in national agricultural policies have prioritized the development of green agriculture and economic restructuring in the eastern and southern regions. Consequently, the coordination level in these areas has gradually transitioned towards a state of basic coordination.
Fig. 4.
Scatter plot of Global Autocorrelation Moran’s Index for Coupling Coordination in 2006, 2011, 2016, and 2021.
Analysis of the Driving Factors Behind the Coupling Coordination Degree
Considering the limitations of the GWR model, which only allows for cross-sectional data [18], we have selected four time points, namely 2006, 2011, 2016, and 2021, to capture the average values of the influencing factors and the coupling coordination degree. Adaptive kernel type and bandwidth have been chosen accordingly. The parameter results of each influencing factor in the GWR model are presented in Table 4, with a model value of 0.6001, indicating a satisfactory fit of the twelve influencing factors in the GWR model.
The impact of green and low-carbon agricultural factors on the coupling coordination degree varies across different regions, as illustrated in Fig. 5. From an analysis of the factors influencing agricultural carbon emissions intensity, it becomes apparent that the eastern and northern regions exhibit a notable positive effect, while the western and southern regions show a negative effect. This can be attributed to the inherent agricultural resource endowment and industrial structure characteristics in China. Provinces such as Jiangsu, Anhui, and Jiangxi predominantly engage in rice cultivation for grain production, which serves as the primary source of agricultural carbon emissions (accounting for over 45% of the total).In Shanghai and Zhejiang, the agricultural industry is primarily focused on cultivation. Production heavily relies on high inputs of energy and agricultural materials, resulting in higher carbon emissions intensity. In regions such as Beijing, Shanxi, Tianjin, and Liaoning, dryland areas are more prevalent, leading to a significant dependence on water resources and substantial irrigation consumption in agricultural fields.
Table 4.
Table of Statistical Tests for GWR Model Parameter Estimation.
| Model Parameter | Value | Model Parameter | Value |
|---|---|---|---|
| bandwidth | 2273396.68 | AICc | 62.2678 |
| Sum of squared residuals | 4.0467 | Goodness of Fit | 0.6001 |
| Significant digits | 12.7883 | Adjust goodness of fit | 0.3412 |
| Sigma value | 0.4714 |
Fig. 5.
Spatial Distribution of Regression Coefficients for Factors Influencing Coupling Coordination of Green Low-Carbon Agriculture from 2003 to 2021.
In Hebei, Jilin, Shandong, and Henan, wheat and corn cultivation takes precedence, accompanied by a thriving livestock and poultry farming industry. In comparison, the southwestern regions of China, characterized by mountainous terrain and high plateaus, are not conducive to large-scale cultivation due to adverse natural conditions. Consequently, these areas exhibit lower carbon emissions intensity and relatively better ecological environments, aligning with the general assumption of a negative impact on the development of green and low-carbon agriculture.
The impact of greening coverage on the harmonious coupling degree between national green and low-carbon agriculture and socio-economic development exhibits a positive correlation. The coefficient in the southern region is more pronounced, signifying a higher degree of greening coverage in the south, whereas the north is comparatively disadvantaged due to climatic and environmental factors. The pattern of the irrigation area ratio of arable land is inversely related to the intensity of agricultural carbon emissions. In the eastern and northern regions, it primarily exerts a negative effect on the harmonious coupling degree between green and low-carbon agriculture and socio-economic development. Conversely, it exhibits a positive effect in the western and southern regions. This correlation can be attributed to the distinctive characteristics of China’s agricultural industry structure. Regions with a predominant focus on cultivation have larger irrigated areas, with significant consumption concentrated in areas such as Zhejiang, Shanghai, Anhui, Liaoning, and Jilin. Similarly, Beijing, Tianjin, Jilin, Shanxi, and other regions face constraints on water resources. Consequently, the irrigation area ratio of these regions does not serve as a positive catalyst for the harmonious coupling of green and low-carbon agriculture with socio-economic development.
The intensity of fertilizer application continues to play a positive role in the harmonious coupling of green and low-carbon agriculture with socio-economic development in the southeastern regions, where cultivation remains predominant. The production process in the cultivation industry inevitably involves high input of agricultural materials such as fertilizers. Conversely, the intensity of pesticide and agricultural film application has a negative impact on the harmonious coupling of green and low-carbon agriculture with socio-economic development in the eastern and northern regions. This is closely related to recent national policies that promote standardized prevention and control of pesticide application, targeted and appropriate use of pesticides, and strict control over their precise application. There has also been increased regulatory oversight on agricultural films, with the implementation of admission and application standards. Provinces such as Zhejiang, Fujian, Shandong, and Jiangsu are leading the way in promoting green agricultural development at the national level. However, in some central regions, the level of green pest control is relatively weaker. For example, in major grain-producing areas for rice, corn, and wheat like Hunan, Hubei, and Jiangxi, a positive effect on the harmonious coupling of green and low-carbon agriculture with socio-economic development is observed.
Fig. 6.
Spatial Distribution of Regression Coefficients for Economic-Social Factors Influencing Coupling Coordination from 2003 to 2021.
From an economic perspective (as depicted in Fig. 6), the per capita GDP exerts a positive impact, with regression coefficients increasing from east to west. These coefficients signify the marginal utility of per capita GDP, which, in turn, exhibits a diminishing marginal utility. Thus, this phenomenon arises as a result. The agricultural production value per unit area has a negative effect, with the highest negative values occurring in the northern regions. These areas have a large agricultural population and relatively underdeveloped agricultural production technology. In contrast, the southern regions are economically developed and have a high degree of coupling and coordination. Rural residents’ disposable income has both positive and negative effects, with a larger proportion in the negative direction, concentrated in the northern regions. The higher positive values are distributed in the south. The increase in rural residents’ disposable income promotes the coordinated development of green and low-carbon agriculture with socio-economic development.
From a social perspective (as depicted in Fig. 6), the degree of influence of agricultural technological progress contribution rate, urban residents’ Engel coefficient, and the proportion of average education years in rural areas is significant. Among them, the urban residents’ Engel coefficient has the most notable impact, mainly exerting a negative effect. The absolute value of the regression coefficient is larger in the northeast region, which is a major grain-producing area and a key focus for the development of green agriculture. In the future, it will be necessary to accelerate the pace of transformation towards green and low-carbon agriculture in these areas. In the western regions, the Engel coefficient is relatively lower, and the population is more dispersed, indicating a lower dependence of the degree of coupling and coordination on the urban residents’ Engel coefficient. The proportion of regions where the contribution rate of agricultural technological progress has a negative effect is significant. At the national level, the level of agricultural technological progress still needs to be further strengthened, and the dependence of the degree of coupling and coordination on it is not strong. The positive and negative impacts of the average education years in rural areas are relatively balanced. In northern rural areas, the negative effect of average education years is evident. This is consistent with the relatively lower economic and social development in the northern regions, where rural residents generally have a lower level of education. Therefore, the degree of coupling and coordination in these areas does not strongly depend on the average education years in rural areas.
Conclusions
Based on an analysis of the mechanisms underlying the interplay between green and low-carbon agriculture, as well as its impact on society and the economy, this study constructs an evaluation index system for the coordinated development of green and low-carbon agriculture and the socio-economic sphere. By employing a model that measures the degree of coupling coordination, the spatiotemporal patterns of the coupling coordination between green and low-carbon agriculture and the socio-economic sphere in 31 provinces (municipalities and autonomous regions) of China from 2003 to 2021 were calculated. Furthermore, leveraging the GWR model, the driving factors behind the coupling coordination between green and low-carbon agriculture and the socio-economic sphere were explored. The main conclusions are as follows:
1. In terms of temporal changes, the development level of green and low-carbon agriculture in China follows a “U-shaped” trajectory, initially declining, then rising, and ultimately stabilizing. The level of societal development has been steadily increasing over the years, while the economic development level exhibits a fluctuating pattern of growth. The national coupling coordination between green and low-carbon agriculture and the socio-economic sphere has transitioned from an antagonistic phase to a phase of gradual adjustment, ultimately reaching a high-level coupling stage. However, the degree of coupling coordination has undergone three stages: “moderate imbalance = mild imbalance - nearing imbalance.” It is yet to achieve a favorable state of coordinated development, indicating significant room for improvement [19].
2. Since 2006, there has been a notable shift in the spatial distribution of the coupling coordination between green and low-carbon agriculture and the socio-economic sphere across China. The spatio-temporal patterns of this coupling coordination have undergone distinct changes. The southeastern region has gradually transitioned from a lower level to a higher level of coordination. In contrast, the western region has predominantly remained at lower levels of coordination. The northern region, on the other hand, has exhibited a fluctuating development pattern, characterized by periods of high coordination, followed by lower coordination, and then returning to high coordination [20].
3. When analyzing the driving factors of coupling coordination between green and low-carbon agriculture and the socio-economic sphere, the impact of different factors varies across regions. Green coverage and per capita GDP have positive influences on the coupling coordination in all 31 provinces, municipalities, and autonomous regions in China. However, the agricultural production value per unit area has a negative impact on the national coupling coordination. The intensity of agricultural carbon emissions, the rate of effective irrigation area, and the intensity of fertilizer, pesticide, and plastic film usage, exhibit both positive and negative effects on the coupling coordination, closely related to the characteristics of China’s agricultural industry structure. Rural residents’ disposable income has a significant negative impact on the coupling coordination in the northern region. The contribution rate of agricultural technological progress and the average years of rural education have positive effects on the coupling coordination in the southern region. The Engel coefficient of urban residents has a noticeable negative impact on the coupling coordination in the northeastern region [21, 22].
