Home Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
Article Open Access

Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry

  • Zhuo Luo and Yongxin Huang EMAIL logo
Published/Copyright: July 10, 2025
Download Article (PDF)
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Open Geosciences
From the journal Open Geosciences Volume 17 Issue 1

Abstract

This study focuses on the sustainable development of agriculture and animal husbandry in ethnic minority regions of China, particularly examining the impact of green organic transformation. Given the increasingly severe global environmental issues, there is growing societal demand for sustainability in agriculture and animal husbandry. Ethnic minority regions face unique challenges and opportunities due to their distinct geographical, environmental, and cultural contexts. Therefore, it is crucial to investigate in depth the effects of green organic transformation on the sustainable development of agriculture and animal husbandry in these regions. Using panel data from 2013 to 2022, this study measures the Agriculture and Animal Husbandry Sustainable Development Index (ASDI) in eight ethnic minority provinces. It comprehensively evaluates sustainability from both overall and detailed perspectives. By constructing a panel threshold model, the study further explores the spillover effects of green organic transformation on the sustainable development of agriculture and animal husbandry in ethnic minority regions, informing targeted policies and strategies. The research indicates that green organic transformation has significantly positive effects on the sustainable development of agriculture and animal husbandry. We will innovate and extend the agricultural and animal husbandry industry chain, increase training for farmers and herdsmen, improve their feasible ability to make green organic transformation, and build an agricultural and animal husbandry development platform of “cooperatives + markets + farmers.” This supports high-quality development in agriculture and animal husbandry in ethnic minority regions. However, the study has limitations such as the time span and sample size of the data, which may constrain the generalizability of the results. Additionally, further research is needed to deepen the understanding of the specific mechanisms of green organic transformation. Future studies should expand the sample size, increase the length of time series, and integrate qualitative analysis methods to comprehensively understand the intrinsic mechanisms of green organic transformation on the sustainable development of agriculture and animal husbandry in ethnic minority regions.

Keywords: agriculture; animal husbandry; green organic transformation; ASDI; spillover effects

1 Introduction

China is a unified multi-ethnic country with a population of more than 100 million ethnic minorities [1]. Ethnic autonomous areas account for 64% of China’s total land area, and the vast majority of western and border areas are inhabited by ethnic minorities. These basic national conditions determine that the development of national minorities and ethnic areas plays an extremely important role in the overall economic and social development of our country. The sustainable development of agriculture and animal husbandry in ethnic minority areas is not only related to national food security, biodiversity security, resource security, and ecological security but also a key link to realize the rural revitalization and ecological civilization construction.

The high-quality development of agriculture and animal husbandry in ethnic regions is not only crucial for national food security, biodiversity safety, resource security, and ecological safety but also serves as a key factor in realizing rural revitalization and building an ecological civilization [2]. The unique characteristics of agriculture and animal husbandry in these regions demand the implementation of green transformation strategies that align with local realities to address the prolonged challenges posed by high input, consumption, and pollution associated with existing development models, ensuring the sustainable development of agriculture and animal husbandry.

The escalating constraints of resource and environment in ethnic regions have shifted the primary contradiction from “total scarcity” to “structural contradiction.” This necessitates not only increased production in agriculture and animal husbandry but also higher-quality output and a more sustainable development path [3]. Achieving the sustainable development of agriculture and animal husbandry has become an inevitable choice for realizing green development and rural revitalization [4]. The importance of sustainable development in agriculture and animal husbandry is self-evident, impacting not only the livelihoods of local farmers and herders but also national food security and the health of the ecological environment. However, accurately assessing the level of sustainable development in agriculture and animal husbandry, exploring the consistency between sustainable development and economic growth in this sector, and identifying obstacles to sustainable development are pressing issues in the current context.

Against this backdrop, the necessity of green organic transformation becomes particularly significant. Transformation implies not only a change in production methods but also entails environmental upgrades and sustainable development across the entire industry chain. This includes the promotion of low-carbon, water-saving, and energy-efficient agricultural and animal husbandry technologies, the development of circular agriculture, and the optimization of planting and breeding structures. In terms of technology and strategy, joint efforts by the government and businesses are required to promote smart agriculture and precision agriculture technologies, enhance training and education for farmers to increase their awareness and acceptance of green agriculture and animal husbandry. Additionally, encouragement for the utilization of “Internet +” and big data technologies to improve the efficiency and output quality of agriculture and animal husbandry is essential. Confronted with challenges such as labor force outflows, difficulties in technology dissemination, and delayed development of the market for green products, the government needs to provide support through policies, funding, and technological initiatives. For instance, incentivizing green agricultural practices through subsidies, tax benefits, and supporting the marketing and branding of green agricultural products [5]. Green low-carbon animal husbandry plays a crucial role in achieving carbon peak and carbon neutrality goals. Promoting the development of animal husbandry toward a low-carbon, environmentally friendly, and circular direction while strengthening the monitoring and management of carbon emissions is essential. The spillover effects of green transformation will enhance ecological efficiency between regions, fostering a scenario of coordinated development. Furthermore, the supply chain effects of green technological innovation contribute to the overall green upgrading of both manufacturing and agriculture, expediting the adjustment and optimization of economic structures [6].

Studying the impact of green organic transformation on the sustainable development of agriculture and animal husbandry in ethnic regions holds significant theoretical and practical value. This transformation may involve adopting organic farming, reducing the use of pesticides and fertilizers, and improving livestock farming practices. Through in-depth research in this field, we can better understand the current state of agriculture and animal husbandry, identify paths to promote sustainable development, and provide valuable insights and policy recommendations for the high-quality development of agriculture and animal husbandry in ethnic regions. The aim of this study is to analyze the spillover effects of green organic transformation on the high-quality development of agriculture and animal husbandry in ethnic regions. Through thorough research and data analysis, we will explore the impact of this transformation on aspects such as resources, environment, society, and the economy, with the aim of offering valuable insights and policy recommendations for the sustainable development of agriculture and animal husbandry in ethnic regions.

2 Literature review

Currently, agriculture and animal husbandry in ethnic regions exhibit various characteristics and unique conditions, encompassing natural factors, ecological environments, production methods, and social cultures, all of which are crucial considerations for green organic transformation. Ethnic regions typically possess distinctive natural and ecological conditions, and agricultural and animal husbandry practices vary across different areas. For instance, Aba County in Sichuan Province has tailored its development of distinctive industries to local conditions and promoted the transformation and upgrading of agriculture and animal husbandry through initiatives such as establishing ecological agricultural research demonstration and training bases [7]. These measures not only strengthen the protection of agricultural ecological environments but also propel the modernization of agriculture and animal husbandry. Furthermore, social culture plays a significant role in the development of agriculture and animal husbandry in ethnic regions. The progress of agriculture and animal husbandry in these areas relies not only on natural resources but is also deeply influenced by local culture and traditions. Developing distinctive agriculture and animal husbandry, preserving and utilizing traditional knowledge and techniques, holds particular importance in these regions. Despite the unique and diverse nature of agriculture and animal husbandry in ethnic regions, along with the advantages derived from traditions, they face challenges related to modernization and sustainable development. Overcoming these challenges requires increased autonomous innovation, deepening the influence of green brands, and strengthening the interconnected interests among agricultural enterprises to drive the adjustment and upgrade of industry structures. Simultaneously pursuing innovation and green organic transformation strategies is essential while promoting the high-quality development of agriculture and animal husbandry.

The green (organic) transformation of agricultural production is a crucial measure for advancing high-quality agricultural development and realizing rural revitalization [8]. It involves effectively connecting, transforming, and mutually supporting capital endowment, external environments, and the green concepts of farmers to create conditions for the green transformation of agricultural production. This includes agricultural production trusteeship, service-oriented scale operation, the deep integration of “Internet +” with agriculture, and the development of green agricultural technologies. At the practical level, China’s green transformation of agricultural production still faces challenges such as the loss of high-quality labor, obstacles in trust for technology dissemination, and a lag in the development of the market for green agricultural products. To address these challenges, strategies such as embedded promotion of agricultural technology, promoting the green transformation of agricultural production trusteeship, and advocating for green agricultural product consumption are proposed. The development of green and low-carbon animal husbandry is a key pathway for achieving carbon peak and carbon neutrality goals [9]. Scientifically tracking and calculating carbon emissions in animal husbandry, formulating plans for carbon peak and carbon neutrality, emphasizing technological research and development for the efficient and green low-carbon development of animal husbandry, and standardizing relevant technical standards are crucial measures to enhance production efficiency [10]. Establishing special funds for the green and low-carbon development of animal husbandry, integrating resources, and promoting the integrated development of animal husbandry with the renewable energy industry are effective ways to advance the green and low-carbon development of animal husbandry [11]. The green organic transformation of agriculture and animal husbandry involves not only improvements in technology and production methods but also a profound understanding of the entire industry chain and strategic adjustments. This has significant positive impacts on enhancing the quality of agricultural products, increasing farmers’ income, and improving the ecological environment.

Green transformation in agriculture and related fields can generate significant spillover effects. In the agricultural sector, green transformation typically aims to achieve more sustainable production methods, reduce environmental pollution, and improve resource efficiency [12]. This transformation can influence social and economic development by enhancing environmental quality and ecological efficiency. Environmental regulations may lead to an improvement in ecological efficiency between regions, and this improvement can, in turn, affect the ecological efficiency of adjacent areas through spatial spillover effects. Moreover, research in the manufacturing sector has indicated that studies on agglomeration effects and crowding-out effects suggest that the agglomeration of manufacturing not only affects local environmental pollution but also influences other cities. This implies that green development can drive the coordinated development of regional industries, achieving an overall higher quality of green development. At the micro level, the spillover effects of green technological innovation in the supply chain are also crucial. Research has found that the green technological innovation of customer enterprises can enhance the green technological innovation of supplier enterprises in terms of quantity and quality. This spillover effect is more pronounced in areas with high customer concentration, non-state-owned enterprises, and customers in the southern region. Additionally, the green technological innovation of customer enterprises has positive effects on the economic and environmental performance of supplier enterprises, revealing the implicit role of the supply chain in driving green technological innovation in manufacturing. This illustrates that green development strategies can not only improve the ecological efficiency and economic development of specific regions but may also have positive indirect effects on neighboring areas. These cross-regional positive effects are one of the significant reasons to promote the green transformation of agriculture and animal husbandry in ethnic regions. The key factors and influencing factors of sustainable development of agriculture and animal husbandry were analyzed, and the spillover effects of the transformation of green organic agricultural and animal husbandry products on sustainable development of agriculture and animal husbandry in ethnic areas were studied.

In summary, agriculture and animal husbandry in ethnic regions currently exhibit particularities due to their unique natural and ecological conditions and diverse production methods influenced by local social and cultural factors. In response to these conditions, green organic transformation becomes a key strategy for enhancing the modernization of agriculture and protecting the ecological environment. This involves agricultural production trusteeship, service-oriented scale operation, the integration of “Internet +,” and the development of green agricultural technologies. Faced with challenges such as labor force outflows, obstacles in technology dissemination, and a delayed development of the market for green agricultural products, strategies including embedded promotion of technology, promoting green transformation, and advocating green consumption are proposed. Additionally, the development of green and low-carbon animal husbandry, as a crucial pathway for achieving carbon peak and carbon neutrality, requires attention to technological research and development and standard formulation. The spillover effects of green transformation not only enhance the ecological efficiency and economic development of specific regions but may also have positive indirect impacts on neighboring areas. The government plays a crucial role in promoting this transformation through the formulation of relevant policies, providing financial support, and facilitating technology dissemination.

3 Research design

This study, based on clarifying the current status of the remaining space in resource and environmental constraints, introduces four indicators: land (grassland) space, water space, environmental space, and technological space. By constructing a panel threshold model, the key factors and influencing elements of sustainable development in agriculture and animal husbandry are analyzed. The study investigates the spillover effects of the green organic transformation of agricultural and animal products on the sustainable development of agriculture and animal husbandry in ethnic regions, aiming to gain a deep understanding of the patterns and obstacles in the sustainable development of agriculture and animal husbandry in ethnic regions.

3.1 Evaluation of the sustainable development index for agriculture and animal husbandry

The evaluation of the Sustainable Development Index for agriculture and animal husbandry establishes a novel approach for assessing sustainable development under resource and environmental constraints [13]. It incorporates comprehensive indicators that quantify the sustainable development of agriculture and animal husbandry across economic, social, and environmental dimensions.

(1) ASDI = w 1 min { A S it ′ , W S it ′ , E S it ′ } + w 2 D S it ,

where ASDI represents the Agriculture and Animal Husbandry Sustainable Development Index [14]. A S it ′ , W S it ′ , E S it ′ are the standardized values of land (grassland), water, and environmental space, respectively. D S it stands for the technological space, and w 1 and w 2 are the weight coefficients.

Farmland (grassland), water, and the environment are irreplaceable in the development of agriculture and animal husbandry. In this study, the minimum value among the three spatial dimensions in equation (1) is considered as the constraint imposed by resource and environmental factors on development. The specific explanations and weighting methods for each indicator are as follows.

3.1.1 Farmland (grassland) pressure index

This index characterizes the size of the farmland (grassland) space. A higher value of this index indicates a greater limitation on the availability of farmland (grassland) resources in the province [15].

3.1.2 Water space

Water space refers to the spatial allocation of current agricultural and animal husbandry water use relative to the maximum available water for agricultural and animal husbandry purposes [16].

(2) W S it = T W it − I W it − D W it − CAW U it | T W it − I W it − D W it | .

Let W S it be the water space, 11 billion cubic meters represent the total water resources, 1 billion cubic meters is for industrial use, D W it stands for the domestic use, which is 1 billion cubic meters. And 1 billion cubic meters is the current water use for agricultural and animal husbandry, which is represented by CAW U it .

3.1.3 Environmental space

The gray water footprint (GWF) is employed to measure the environmental pollution.

(3) E S ir = T W it − IGW F it − DGW F it − CGW F it | T W it − IGW F it − DGW F it | ,

where E S ir represents the environmental space; IGW F it stands for industrial GWF ; DGW F it stands for the domestic GWF ; and CGW F it represents the current GWF in agriculture and animal husbandry.

For the purpose of comparison, the values are further normalized within the range of 0–1.

(4) A S it ′ = max { A S ir } − A S ir max { A S it } − min { A S ir } ,

(5) W S it ′ = W S it − min { W S ir } max { W S it } − min { W S ir } ,

(6) E S it ′ = E S ir − min { E S it } max { E S ir } − min { E S ir } .

3.1.4 Technological space

Utilizing the gap between the current level of mechanization and the highest level of mechanization to reflect the technological space.

(7) D S it = max { A M it } − A M it max { A M it } .

In the above equation, D S it represents the technological space, A M it denotes the level of mechanization in agriculture and animal husbandry, defined as the ratio of the total power of agricultural machinery to the area of cultivated land (grassland).

All four indicators mentioned above are expressed as relative indicators, i.e., (maximum available space – current agricultural and animal husbandry utilization space)/maximum available space, indicating the proportion of current remaining space to the maximum available space. The larger the ratio, the larger the space, often indicating greater sustainability.

After defining these four spaces, their values range from 0 to 1. Approaching 0 indicates a small development space, while approaching 1 indicates a large development space. In other words, ASDI gradually tends toward a sustainable development state from 0 to 1.

3.1.5 Entropy weight method

The entropy weight method is employed to determine the weights, following the steps below.

Step 1: Calculate the contribution p irj of each index j to indicator i in the t-th year.

(8) p irj = x itj ∑ i = 1 31 ∑ r = 1 20 x itj .

Step 2: Calculate the entropy E j of indicator j.

(9) E j = − k ∑ i = 1 31 ∑ i = 1 20 p iji ln p itj .

The constant k is generally chosen as k = 1 / ln ( t * i ) .

Step 3: Calculate the diversity coefficient of indicator j.

(10) D j = 1 − E j .

The D j represents the diversity of contributions for each option to indicator j.

Step 4: Determine the weighting coefficient w j .

(11) w j = D j ∑ j = 1 2 D j .

3.2 Measurement of the green organic transformation of agricultural and livestock products

Estimation of the transformation toward green and organic agricultural and livestock products in farming and animal husbandry [17].

(12) L Q ij = q ij q j q i q ,

where L Q ij represents the output of the industry in the i location in province j, q i represents the total output of the industry in province j, q ij represents the output of the industry in the i location in China, and q represents the total output of the industry in China. The calculation focuses on the agglomeration of the agricultural and livestock industries, hence considering only the output of the primary industry (i = 1).

This study, building upon previous studies, employs two indicators – industrial structure rationalization and superization – to investigate the transformation of green and organic agricultural and livestock products. The entropy index is utilized to measure the transformation of green and organic agricultural and livestock products.

(13) ISR = ∑ i = 1 n Y i Y ln Y i L i / Y L ,

where ISR represents the transformation of green organic agricultural and livestock products, Y denotes the total output, Y i is the output of the i sector, and L stands for total employment, and L i is the employment in the i sector.

Utilizing the ratio of the output value of the tertiary industry to the output value of the secondary industry to reflect the transformation of green organic agricultural and livestock products, we obtain

(14) ISS = Y 3 Y 2 ,

where ISS represents the industrial structure surplus, and Y 2 and Y 3 represent the output of the second and third industries, respectively. The higher the proportion of the tertiary industry, the higher the degree of economic surplus. Referring to existing literature, this study assigns equal weights to these two indicators.

(15) UP = 0.5 * ISR + 0.5 * ISS ,

where UP stands for the transformation of green organic agricultural and livestock products.

3.3 Impact model of green organic agricultural and livestock product transformation on sustainable development of agriculture and animal husbandry

Establishing the corresponding panel model,

(16) ASD I if = α + β 1 LQ + β 2 U P if + β 3 X if + ε if ,

where X represents control variables, ε is the random error term, α is the intercept term, i denotes the individuals, and t is the time. Introducing a panel threshold model, the nonlinear impact of the transformation of green organic agricultural and livestock products on the sustainable development of agriculture and animal husbandry is analyzed. The model selects a specific variable as the threshold variable, determines different groups for all samples, and assigns different regression equations to each group. In this study, lnPGDP, representing the level of economic development, is chosen as the threshold variable, and the panel threshold regression model is established as follows:

(17) ASD I it = θ i + β 11 L Q it I ( ln ⁡ PGDP < γ 1 ) + β 12 L Q it I ( γ 1 < ln ⁡ PGDP < γ 2 ) + β 13 L Q it I ( ln ⁡ PGDP > γ 2 ) + β 14 U P it + β 15 X it + μ it ,

(18) ASD I it = ω i + β 21 U P it I ( ln ⁡ PGDP < τ 1 ) + β 22 U P il I ( τ 1 < ln ⁡ PGDP < τ 2 ) + β 23 U P it I ( ln ⁡ PGDP > τ 2 ) + β 24 L Q it + β 25 X it + σ it ,

where PGDP represents per capita gross domestic product (GDP), ln denotes the natural logarithm, θ i and ω i are the individual effects, γ and τ are thresholds, and μ it and σ it are the random disturbance terms. Equation (17) examines the nonlinear effects of agricultural and animal husbandry industry agglomeration on the sustainable development of agriculture and animal husbandry, while equation (18) examines the nonlinear effects of industrial structure upgrading on the sustainable development of agriculture and animal husbandry. Both will serve ln PGDP as threshold variables. The data came from official government documents of Inner Mongolia Autonomous Region, Ningxia Hui Autonomous Region, Xinjiang Uygur Autonomous Region, Tibet Autonomous Region, Guangxi Zhuang Autonomous Region, Guizhou Province, Yunnan Province, and Qinghai Province.

Referring to relevant studies, the following variables are selected as control variables: FE, the proportion of fiscal expenditure on agriculture and animal husbandry; CY, grain yield per unit area; and UR, urbanization rate. Values of each variable are provided in Table 1.

Table 1

Descriptive statistics

Variable Sample size Mean Standard deviation Minimum Maximum
A 13,024 0.2556 0.0395 0.2865 0.9356
B 13,024 0.3295 0.2415 0.0049 0.8844
C 13,024 9.0045 0.6359 8.7285 10.0458
D 13,024 10.1847 0.1922 9.5395 11.0045
E 13,024 11.5589 13.4725 0.0528 83.1246
F 13,024 8.0256 9.0458 0.2669 88.0589
G 13,024 0.6953 0.2284 0.0058 2.7184
H 13,024 0.2265 0.3295 0.0034 3.2956
I 13,024 2.8422 2.1845 0.0049 24.1845
J 13,024 0.5982 0.5392 0.5293 1.8265
K 13,024 0.0356 0.0418 0.0046 0.2295

4 Empirical results and analysis

4.1 Basic panel model regression results

In 2005, the General Office of the CPC Central Committee and the General Office of the State Council jointly issued the Opinions of the CPC Central Committee and the State Council on Further Strengthening Ethnic Work and Accelerating the Economic and Social Development of Ethnic Minorities and Ethnic Minority Areas. Along with this document and other related provisions, special policies have also been formulated to suit the actual conditions of various ethnic minority regions. These include policy documents aimed at promoting development in Tibetan areas, as well as in Xinjiang, Ningxia, Guangxi, Yunnan border areas, and Qinghai Province. The policies encompass both preferential measures to boost rapid economic development – such as supporting ethnic trade and the production of commodities catering to the special needs of ethnic groups – and concrete initiatives to advance social development, such as the issuance of several opinions on promoting the prosperity of ethnic minority cultural undertakings. Table 2 presents the regression results of four different types of models on the impact of the transformation of green organic farming and animal husbandry products on the sustainable development of agriculture and animal husbandry. The results of Model 1 indicate a positive impact of the green organic transformation on the sustainable development of agriculture and animal husbandry. This is evident from its coefficient of 0.1458, which is statistically significant at the 1% level (p-value < 0.01). This finding highlights the crucial role of green transformation in promoting the sustainability of agriculture and animal husbandry. The consistency of this positive impact across various models further confirms the catalytic role of green transformation in driving sustainable development in the industry. In contrast, industrial structure upgrading in Model 1 shows a negative impact on sustainable development, reflecting challenges in the structural adjustment process.

Table 2

Benchmark regression results of the impact of agricultural high-quality development on agricultural green organic transformation

Variable (1) (2) (3)
Dige 0.1458*** (0.0356) 0.2756*** (0.0425) 0.2395** (0.0649)
Lnrgdp 0.0854***
(0.0037)
Lnwage 0.0294***
(0.0257)
Hum −0.0458**
(0.0038)
Urban −0.0294***
(0.0027)
Lnstruc 0.0386***
(0.0283)
Mar −0.0695***
(0.0054)
Lnfina 0.0256***
(0.0386)
Finadp 0.0968**
(0.0184)
Fdi 0.2285***
(0.0346)
_Cons 0.3056*** 0.4395*** −0.2956***
(0.0029) (0.0054) (0.1058)
Fixed effects by year Control Uncontrolled Control
Fixed effects by city Control Control Uncontrolled
Within R 2 0.6475 0.6512 0.5104
Sample size 2,398 2,398 2,092

**p < 0.01 indicates a stronger significance (highly significant) and ***p < 0.001 indicates extremely significant (highly significant) results.

Furthermore, we expanded the models by introducing multiple control variables, such as regional GDP, wages, human capital, urbanization, industrial structure, marketization, financial development, financial adaptability, and foreign direct investment. In these models, the positive impact of green organic transformation remains significant, although its influence slightly diminishes. For example, in Model 2, the coefficient is 0.2395, still statistically significant at the 5% level (p-value <0.05). This result suggests that, even when considering a wide range of economic and social factors, the positive effect of green transformation remains evident.

Model 3 further provides insights into the factors influencing sustainable development. Positive correlations between regional GDP and wages with sustainable development imply that economic growth and higher wage levels may contribute to achieving sustainability goals. However, the negative effects of human capital and urbanization suggest that rapid urbanization and the demand for higher education may pose challenges to achieving sustainability. The positive effect of industrial structure upgrading in this model, in contrast to the results of Model 1, prompts further analysis of this difference.

Furthermore, the negative impact of marketization emphasizes the risks associated with rapid market reforms, which, without proper regulation, may contradict sustainable practices. Positive correlations observed for financial development and financial adaptability policies imply that favorable financial conditions and flexible financial policies play a role in driving sustainability in agriculture and animal husbandry. The strong positive correlation of foreign direct investment underscores the importance of international capital in promoting the sector’s transition to sustainability. The negative impact of marketization suggests that without an adequate regulatory framework, rapid market reforms may be inconsistent with binding sustainable practices. Positive correlations for financial development indicate that better financial conditions can support sustainable development. The positive effect of financial adaptability policies highlights the importance of adaptive financial policies in sustainability. The strong positive coefficient for foreign direct investment suggests that foreign investment can significantly drive the agricultural and animal husbandry sectors toward sustainability. Additionally, in explaining the variation within the data, Models 1 and 2 demonstrate similar capabilities with R-squared values of 0.6475 and 0.6512, respectively, indicating that annual fixed effects do not significantly alter the explanatory power of the models. However, the slightly lower R-squared value for Model 3 (0.5104) may result from the addition of more control variables and the exclusion of urban fixed effects.

In conclusion, the regression results confirm the positive role of green organic transformation in promoting the sustainable development of the agricultural sector. However, the impacts of other economic factors and the urbanization process present a more complex picture, indicating that while certain developments and financial adaptability aspects are favorable for sustainability, other factors such as human capital and urbanization may pose challenges. This calls for targeted policy interventions. The differences in model outcomes under different controls emphasize the importance of considering a broad range of socio-economic factors in the policy formulation and implementation process.

4.2 Intrinsic mechanism study

To explore various mechanisms influencing agricultural high-quality development through the green organic transformation in animal husbandry, the control variables, fixed effects, and mediation effects are considered, contributing to the understanding of the complex relationships involved in the process. Table 3 presents results from a comprehensive analysis examining the mechanisms of agricultural high-quality development through the green organic transformation in animal husbandry. Different variables are explored across multiple models. And control variables are applied consistently across all models to account for relevant factors. In addition, the presence of year fixed effects is controlled in all models. City fixed effects are uncontrolled in Models (1)–(3), while controlled in Models (4)–(6). In addition, the Sobel Z test statistic is reported for Model (2), revealing a significant mediation effect.

Table 3

Test results for the mechanisms of agricultural high-quality development through the green organic transformation in animal husbandry

Variable (1) (2) (3) (4) (5) (6)
lw Hpqa twe Hpqa powf Hpqa
pwe −0.2845*** (0.0959) 0.4275*** (0.0295) −3.0458*** (0.7284) 0.2563*** (0.0095) −0.428* (0.1985) 0.2284 (0.0355)
lw
twe −0.045**
(0.0023)
powf −0.0554**
(0.0095)
Control variables Control Control Control Control Control Control
_Cons −0.5265** −0.4275*** 12.045*** −0.9958** 3.286 0.6175*
(0.3845) (0.0928) (1.8224) (0.0427) (0.1724) (0.0061)
Year fixed effects Control Control Uncontrolled Control Control Control
City fixed effects Uncontrolled Uncontrolled Control Control Control Control
Within R 2 0.1976 0.7584 0.3945 0.6623 0.5542 0.5049
Sample size 2,046 2,046 1,822 1,822 1,937 1,937
Mediation effect 0.0073 0.0542 0.0027
Sobel Z 7.882***

*p < 0.05 indicates statistical significance (significant), **p < 0.01 indicates a stronger significance (highly significant), and ***p < 0.001 indicates extremely significant (highly significant) results.

4.3 Robustness test

In the robustness test section of this study, we employed instrumental variable (IV) analysis and exogenous shock tests to investigate the impact of urbanization rate on the high-quality development of agriculture.

4.3.1 IV analysis

As shown in Table 4, in the study of the influence of urbanization rate on agricultural high-quality development, we initially conducted IV analysis as part of the robustness test. The application of IV analysis aims to address potential endogeneity issues and ensure more accurate model estimation. We selected iv_Dige as the IV. The results of the first-stage test indicate a significant positive correlation between urbanization rate and agricultural high-quality development, providing a solid foundation for the second-stage test. In the second stage, the coefficient of the positive impact of urbanization rate on agricultural high-quality development is 0.3595 (standard error = 0.0725), reaching a three-star significance level. This suggests a significant positive effect of urbanization rate on agricultural high-quality development.

Table 4

Robustness test results for the high-quality development of agriculture

Variable IV method Exogenous shock test
(1) (2) (3)
First stage Second stage Hqda
Dige Hqda
iv_Dige 0.005*** (0.0012) 0.3595*** (0.0725)
Dige 0.0914***
(0.0273)
Hydige 0.8824***
(0.0315)
Control variables Control Control Uncontrolled
_Cons −0.0233** 0.4286*** −0.3955***
(0.0959) (0.0028) (0.0725)
Year fixed effects Uncontrolled Control Control
City fixed effects Control Uncontrolled Uncontrolled
Kleibergen-Paap rk LM statistic 21.3856
[0.0031]
Kleibergen-Paap rk Wald F statistic 28.1754
{16.2845}
Within R 2 0.1395 0.7175 0.6845
Sample size 1,988 1,988 1,988

**p < 0.01 indicates a stronger significance (highly significant) and ***p < 0.001 indicates extremely significant (highly significant) results.

4.3.2 Exogenous shock test

Furthermore, to further validate the robustness of the aforementioned findings, we conducted an exogenous shock test. In Model (3), the coefficient for the impact of urbanization rate on agricultural high-quality development is 0.0914, and simultaneously, the impact coefficient of Hydige on agricultural high-quality development is 0.8824. Both coefficients reach a three-star significance level, indicating significant positive effects of both factors on agricultural high-quality development. This further strengthens our research findings. In model design, we employed differentiated fixed-effects control methods in various models. Year fixed effects were controlled in Models (2) and (3), while city fixed effects were controlled in Model (1). This approach aids in more accurately identifying and understanding relationships between variables. The Within R 2 values of the models demonstrate their different explanatory abilities for agricultural high-quality development.

In terms of model design, control variables were adequately managed in most models to eliminate interference from other factors. Year fixed effects were controlled in Models (2) and (3), while city fixed effects were controlled in Model (1). This differentiated control helps more accurately identify and understand relationships between variables. Additionally, the Kleibergen-Paap rk LM statistic is 21.3856 (p-value = 0.0031), and the Kleibergen-Paap rk Wald F statistic is 28.1754 (critical value = 16.2845), both indicating the effectiveness of the IV method. The different Within R 2 values (Model (1): 0.1395, Model (2): 0.7175, Model (3): 0.6845) reflect variations in the models’ explanatory power for Hqda. Regarding sample size, all models have a sample size of 1,988, providing a sufficient foundation for the stability and reliability of the study results.

4.4 Heteroscedasticity test

The model was subjected to a heteroskedasticity test to ensure its robustness. By introducing control variables and fixed effects, the model demonstrated strong explanatory power, effectively analyzing the relationship between agricultural quality development and urbanization rates even under heteroskedastic conditions. Heteroscedasticity testing shows a correlation coefficient of 0.0818 between urbanization rate and agricultural high-quality development (significant at a two-star level, standard error = 0.0428), further confirming the significant correlation between variables. The application of control variables and fixed effects, along with a model with a Within R 2 value of 0.5826, demonstrates the model’s strong explanatory power. While the sample size slightly decreases in the heteroscedasticity test (1,702), it remains sufficient to support the effectiveness of the analysis (Table 5).

Table 5

Heteroscedasticity test results

Variable Hqda
Dige 0.0818**
(0.0428)
Control variables Control
_Cons 0.2598***
(0.0428)
Year fixed effects Control
City fixed effects Control
Within R 2 0.5826
Sample size 1,702

**p < 0.01 indicates a stronger significance (highly significant) and ***p < 0.001 indicates extremely significant (highly significant) results.

4.5 Spillover effects

In theory, the external characteristics of the green and organic transformation of agriculture and animal husbandry favor the free flow of agricultural resource elements across sectors and regions. The resulting spillover effects of technological innovation are expected to promote the high-quality development of agriculture and animal husbandry in ethnic regions. This study examines the spatial spillover effects of the green and organic transformation of agriculture and animal husbandry on ethnic agriculture and animal husbandry. Before conducting spatial econometric analysis, the study employed the Global Moran’s I index method to calculate the spatial autocorrelation of the green and organic transformation of agriculture and animal husbandry in ethnic regions for each year, using a geographical distance matrix [18]. 100 pilot questionnaires were conducted in open Spaces in areas of rural multi-ethnic communities in south-west Nigeria, sampling respondents’ perceptions of their attachment and utilizing characteristics. Validation variables were used to measure respondents’ attachment attributes and degree of utilization. Table 6 presents the impact of the green and organic transformation of agriculture and animal husbandry on the spatial characteristics of ethnic regions, with Moran’s I index used to assess spatial positive correlation. At a 1% significance level, Moran’s I index is significantly positive, indicating a positive spatial correlation between the green and organic transformation of agriculture and animal husbandry in ethnic regions over the sample period. Specifically, with the increase in years, the Moran’s I index gradually increases, and the Z-value correspondingly rises, demonstrating the significance of this positive correlation.

Table 6

Spatial correlation features of green and organic transformation of agriculture and animal husbandry in ethnic regions from 2013 to 2022

Year Dige Hqda
Moran’s I Z-value Moran’s I Z-value
2013 0.1573* 10.3738 0.0234*** 4.8724
2014 0.1239*** 10.7823 0.0839* 5.1562
2015 0.1285*** 11.7965 0.0485*** 5.9884
2016 0.0195*** 11.9865 0.0695*** 5.9865
2017 0.365*** 12.0046 0.03175*** 6.9384
2018 0.1595*** 12.6698 0.0982*** 6.8958
2019 0.0728*** 12.2956 0.0274*** 7.1445
2020 0.1546*** 15.0475 0.0968** 8.0118
2021 0.2284*** 16.0428 0.0029*** 8.0457
2022 0.2814*** 18.4756 0.0625*** 9.6395

*p < 0.05 indicates statistical significance (significant), **p < 0.01 indicates a stronger significance (highly significant), and ***p < 0.001 indicates extremely significant (highly significant) results.

In this study, we employed a spillover model to analyze the relationship between industrial structure upgrade and the sustainable development of agriculture and animal husbandry. Table 7 indicates that the thresholds are 8.651 and 9.433, respectively. Therefore, for PGDP, the two thresholds for industrial structure upgrade are 5,716 (e8.651) and 12,494 (e9.433). The regression results are presented in Table 7. As the level of economic development increases, the impact of industrial structure upgrade on the sustainable development of agriculture and animal husbandry becomes negative. When PGDP is below 5,716, the impact of industrial structure upgrade on the sustainable development of agriculture and animal husbandry is positive.

Table 7

Analysis of spillover effects of green and organic transformation of agriculture and animal husbandry on ethnic regions

Model setting SAR SDM
α 0.8158*** (0.0258) 0.2846* (0.0685)
Dige 0.968*** 0.2294
(0.0374) (0.0946)
W*Dige 0.4695***
(0.8244)
Control variables Control Control
Year fixed effects Control Control
City fixed effects Control Control
Direct effect 0.2956* 0.2295***
(0.0458) (0.0376)
Spillover effect 1.2956* 1.2856*
(0.6953) (0.6625)
Total effect 1.845*** 0.9185***
(0.5172) (0.2938)
LogL 5581.3956 5628.1355
Within R 2 0.5048 0.6073

*p < 0.05 indicates statistical significance (significant) and ***p < 0.001 indicates extremely significant (highly significant) results.

5 Discussion

5.1 Green and organic transition from an international perspective

The successful experiences of implementing green and organic transitions in agriculture and animal husbandry in China’s ethnic minority regions offer valuable references for other parts of the world facing similar challenges. For instance, in some remote mountainous areas of India, where complex terrain and poor transportation conditions prevail, local communities are also grappling with environmental pressures and economic difficulties caused by traditional agricultural practices. By promoting ecological farming techniques and establishing cooperative models, these regions have not only increased crop yields but also significantly improved the quality of their local ecological environments.

In sub-Saharan Africa, many countries are actively exploring green agricultural development paths suited to their national contexts. For example, in Kenya, the introduction of efficient water-saving irrigation technologies and the use of organic fertilizers have effectively alleviated water scarcity issues and promoted the sustainable development of local agricultural production [19]. Moreover, the Kenyan government encourages smallholder farmers to participate in international fair-trade networks, which has not only increased farmers’ incomes but also opened up new avenues for rural revitalization.

In Latin America, Brazil has adopted a different strategy to address the development challenges in the Amazon rainforest region [20,21]. The Brazilian government, in cooperation with non-governmental organizations, has implemented sustainable forest management programs in indigenous communities. These initiatives aim to protect biodiversity while improving the living standards of local residents. This approach highlights the importance of both cultural and ecological preservation and demonstrates how local resources can be utilized to promote economic development without harming the natural environment.

A comparative analysis of the above cases reveals that although the socioeconomic contexts of these countries and regions differ, they all share a common emphasis on identifying solutions tailored to local conditions and leveraging the collaborative power of governments, enterprises, and communities. This provides important insights for the future development of China’s ethnic minority regions: on the one hand, there is a need to continue deepening the understanding and practice of green and organic transformation; on the other hand, it is crucial to draw upon international best practices and explore more diversified development models, thereby promoting high-quality regional economic growth and comprehensive social progress.

In conclusion, placing the green and organic transition of agriculture and animal husbandry in China’s ethnic minority regions within a broader international context not only enriches the scope of research but also offers new perspectives for scholars at home and abroad. Future studies could further explore the specific mechanisms and influencing factors of green transitions under different cultural backgrounds, in order to better serve the global goals of sustainable development.

This expansion not only enhances the global perspective of the article but also attracts readers from different countries and regions, thereby increasing the impact of the research findings.

6 Conclusion

The investigation into the temporal dynamics of the ASDI unveiled a fluctuating national trend, with critical turning points in 2013 and 2016. This trajectory, marked by an initial rise, a downturn, and a subsequent recovery, underscores the complexity of achieving sustainable development in these sectors. Spatial disparities, particularly prominent in western provinces and southeastern coastal areas, manifested in a 2019 ASDI reduction, primarily attributed to limitations in arable and pasture land alongside environmental pressures.

Our empirical analysis confirmed the positive yet diminishing impact of green organic transformation on agricultural and animal husbandry sustainability, with the benefits tapering off once regional GDP surpasses 7,288 yuan. Moreover, we found that industrial structure upgrading initially bolsters sustainability but transitions into a hindrance when per capita GDP crosses 12,494 yuan, highlighting a complex, nonlinear relationship. This dual-stage dynamic – where China is promoting green organic transformation while simultaneously encountering constraints in industrial restructuring – presents a nuanced landscape of challenges and opportunities.

Given these findings, the following evidence-based recommendations are proposed to facilitate sustainable and high-quality development in ethnic region agriculture and animal husbandry: Policy Implementation and Incentives: To expedite the green organic transition, comprehensive policy frameworks must be introduced, offering incentives such as financial subsidies, tax relief, and preferential loans to farmers and enterprises adopting sustainable practices. These measures should be complemented by educational campaigns and training programs aimed at fostering awareness and acceptance of green technologies among all stakeholders. Labor Force and Technology Challenges: Addressing the exodus of labor from rural areas and facilitating technology diffusion is paramount. Integrating agricultural technology promotion into government strategies can mitigate these issues. Establishing technology transfer platforms and leveraging government-supported programs to disseminate sustainable farming methods are essential. Embracing digital innovations, including “Internet +” and big data analytics, will boost operational efficiency and product quality.

The interventions strike a complex balance between economic development and environmental protection for the sustainable and green development of agriculture and animal husbandry in China’s ethnic minority areas, thus ensuring a harmonious combination of economic growth and ecological sustainability.

Acknowledgments

The authors would like to show sincere thanks to those who have contributed to this research.

  1. Funding information: This work was supported by the Guangzhou Xinhua University University-Level Research Project (2024KYZDSK02); National Social Science Fund Project (18XMZ041).

  2. Author contributions: Zhuo Luo: Zhuo Luo led the design and implementation of the study. She was responsible for data collection and analysis, constructing the panel threshold model, and conducting a detailed evaluation of the Agriculture and Animal Husbandry Sustainable Development Index (ASDI). Additionally, she wrote the main sections of the paper and provided innovative insights into green organic transformation strategies. Yongxin Huang: He provided important theoretical guidance during the design phase of the study and participated in data analysis and interpretation of results. He paid special attention to the literature review section, ensuring its logical structure and coherence, and added discussions on the socio-cultural impacts of green organic transformation. In addition, Yongxin Huang assisted in reviewing and revising the entire manuscript to ensure clarity and accuracy of the arguments.

  3. Conflict of interest: The authors declare that they have no conflicts of interest.

  4. Data availability statement: The tables used to support the findings of this study are included in the article.

References

[1] Kasimi B, Boukir A. Managing multi-ethnicity in China, challenges at home and abroad. Int J Civiliz Stud Tolerance Sci. 2024;1(1):32–43.10.54878/e1vg2b66Search in Google Scholar

[2] Naylor R. Expanding the boundaries of agricultural development. Food Secur. 2011;3:233–51.10.1007/s12571-011-0123-6Search in Google Scholar

[3] Chen C, Zhang H. Evaluation of green development level of Mianyang agriculture, based on the entropy weight method. Sustainability. 2023;15(9):7589.10.3390/su15097589Search in Google Scholar

[4] Jun H, Xiang H. Development of circular economy is a fundamental way to achieve agriculture sustainable development in China. Energy Procedia. 2011;5:1530–4.10.1016/j.egypro.2011.03.262Search in Google Scholar

[5] Wang X, Zhang J, Ma D, Sun H. Green agricultural products supply chain subsidy scheme with green traceability and data-driven marketing of the platform. Int J Environ Res Public Health. 2023;20(4):3056.10.3390/ijerph20043056Search in Google Scholar PubMed PubMed Central

[6] Wang S. The positive effect of green agriculture development on environmental optimization: Measurement and impact mechanism. Front Environ Sci. 2022;10:1035867.10.3389/fenvs.2022.1035867Search in Google Scholar

[7] Zhang H, Duan Y, Han Z. Research on spatial patterns and sustainable development of rural tourism destinations in the Yellow River Basin of China. Land. 2021;10(8):849.10.3390/land10080849Search in Google Scholar

[8] Xingping L. Exploring in the approach to rural revitalization in the view of green development. In IOP Conference Series: Earth and Environmental Science. Vol. 310, No. 5, IOP Publishing; 2019, August. p. 052074.10.1088/1755-1315/310/5/052074Search in Google Scholar

[9] Shi J, Cao X, Chen Z. Pathways for integrating the concept of carbon neutrality into the talent cultivation process: A case study of animal production programs in Chinese agricultural colleges and universities. Sustainability. 2023;15(23):16317.10.3390/su152316317Search in Google Scholar

[10] Lai X, Shi Q. Green and low-carbon technology innovations. In Innovation strategies in environmental science. Amsterdam, Netherlands: Elsevier; 2020. p. 209–53.10.1016/B978-0-12-817382-4.00007-1Search in Google Scholar

[11] Zhang Y, Dong X, Wang XC, Liu M, Zhang P, Liu R, et al. Study on the relationship between low-carbon circular farming and animal husbandry models and human well-being: A case study of Yongchang County, Gansu Province. Sustainability. 2022;14(14):8230.10.3390/su14148230Search in Google Scholar

[12] Mikhno I, Koval V, Shvets G, Garmatiuk O, Tamošiūnienė R. Green economy in sustainable development and improvement of resource efficiency. Prague, Czech Republic: Prague University of Economics and Business; 2021.10.18267/j.cebr.252Search in Google Scholar

[13] Yu W. Research on the measurement of green development level of agriculture and animal husbandry in Haixi Prefecture. In E3S Web of Conferences. Vol. 236, EDP Sciences; 2021. p. 04001.10.1051/e3sconf/202123604001Search in Google Scholar

[14] Zhan X, Xin S, Shao C, Yang F, Long Y. Study of the spatio-temporal variation of agricultural sustainability at national and provincial levels in China. Sustainability. 2023;15(22):15959.10.3390/su152215959Search in Google Scholar

[15] Li X, Lyu X, Dou H, Dang D, Li S, Li X, et al. Strengthening grazing pressure management to improve grassland ecosystem services. Glob Ecol Conserv. 2021;31:e01782.10.1016/j.gecco.2021.e01782Search in Google Scholar

[16] Hsiao TC, Steduto P, Fereres E. A systematic and quantitative approach to improve water use efficiency in agriculture. Irrig Sci. 2007;25:209–31.10.1007/s00271-007-0063-2Search in Google Scholar

[17] Verburg RW, Verberne E, Negro SO. Accelerating the transition towards sustainable agriculture: The case of organic dairy farming in the Netherlands. Agric Syst. 2022;198:103368.10.1016/j.agsy.2022.103368Search in Google Scholar

[18] Luo G, Wang B, Li R, Luo D, Wei C. Study of the agglomeration characteristics of cultivated land in underdeveloped mountainous areas based on spatial auto-correlation: A case of Pengshui County, Chongqing, China. Land. 2022;11(6):854.10.3390/land11060854Search in Google Scholar

[19] Wainaina GK. Challenges in adoption of water‐saving practices: Perspective from drip irrigation adoption in Kenya. World Water Policy. 2021;7(1):74–87.10.1002/wwp2.12048Search in Google Scholar

[20] Stabile MC, Guimarães AL, Silva DS, Ribeiro V, Macedo MN, Coe MT, et al. Solving Brazil’s land use puzzle: Increasing production and slowing Amazon deforestation. Land Use Policy. 2020;91:104362.10.1016/j.landusepol.2019.104362Search in Google Scholar

[21] Garrett RD, Cammelli F, Ferreira J, Levy SA, Valentim J, Vieira I. Forests and sustainable development in the Brazilian Amazon: History, trends, and future prospects. Annu Rev Environ Resour. 2021;46(1):625–52.10.1146/annurev-environ-012220-010228Search in Google Scholar
Received: 2024-06-28
Revised: 2025-05-22
Accepted: 2025-06-04
Published Online: 2025-07-10

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Research Articles
  2. Seismic response and damage model analysis of rocky slopes with weak interlayers
  3. Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
  4. Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
  5. GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
  6. Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
  7. Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
  8. Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
  9. Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
  10. The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
  11. Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
  12. Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
  13. Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
  14. Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
  15. Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
  16. Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
  17. Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
  18. Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
  19. Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
  20. Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
  21. Research on the variation in the Shields curve of silt initiation
  22. Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
  23. Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
  24. Analysis of the formation process of a natural fertilizer in the loess area
  25. Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
  26. Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
  27. Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
  28. Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
  29. Estimating Bowen ratio in local environment based on satellite imagery
  30. 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
  31. Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
  32. Study on the mechanism of plant root influence on soil properties in expansive soil areas
  33. Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
  34. Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
  35. Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
  36. Predicting coastal variations in non-storm conditions with machine learning
  37. Cross-dimensional adaptivity research on a 3D earth observation data cube model
  38. Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
  39. Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
  40. Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
  41. Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
  42. Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
  43. Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
  44. Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
  45. Identification of radial drainage networks based on topographic and geometric features
  46. Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
  47. Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
  48. Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
  49. Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
  50. Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
  51. An efficient network for object detection in scale-imbalanced remote sensing images
  52. Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
  53. Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
  54. A modified Hoek–Brown model considering softening effects and its applications
  55. Evaluation of engineering properties of soil for sustainable urban development
  56. The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
  57. Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
  58. Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
  59. Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
  60. 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
  61. Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
  62. Land use classification through fusion of remote sensing images and multi-source data
  63. Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
  64. Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
  65. Factors impacting spatial distribution of black and odorous water bodies in Hebei
  66. Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
  67. Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
  68. Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
  69. Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
  70. Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
  71. Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
  72. Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
  73. Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
  74. Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
  75. Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
  76. Estimating the travel distance of channelized rock avalanches using genetic programming method
  77. Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
  78. New age constraints of the LGM onset in the Bohemian Forest – Central Europe
  79. Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
  80. Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
  81. Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
  82. Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
  83. Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
  84. Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
  85. Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
  86. Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
  87. Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
  88. Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
  89. A test site case study on the long-term behavior of geotextile tubes
  90. An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
  91. Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
  92. Comparative effects of olivine and sand on KOH-treated clayey soil
  93. YOLO-MC: An algorithm for early forest fire recognition based on drone image
  94. Earthquake building damage classification based on full suite of Sentinel-1 features
  95. Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
  96. Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
  97. An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
  98. Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
  99. Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
  100. Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
  101. A metaverse-based visual analysis approach for 3D reservoir models
  102. Review Articles
  103. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  104. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  105. Ore-controlling structures of granite-related uranium deposits in South China: A review
  106. Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
  107. 10.1515/geo-2025-0861
  108. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
  109. Depopulation in the Visok micro-region: Toward demographic and economic revitalization
  110. Special Issue: Geospatial and Environmental Dynamics - Part II
  111. Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
  112. Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
  113. Minerals for the green agenda, implications, stalemates, and alternatives
  114. Spatiotemporal water quality analysis of Vrana Lake, Croatia
  115. Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
  116. Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
  117. Regional patterns in cause-specific mortality in Montenegro, 1991–2019
  118. Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
  119. Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
  120. Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
  121. Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
  122. Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
  123. Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
  124. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
https://doi.org/10.1515/geo-2025-0824
Keywords for this article
agriculture; animal husbandry; green organic transformation; ASDI; spillover effects
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. Seismic response and damage model analysis of rocky slopes with weak interlayers
  3. Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
  4. Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
  5. GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
  6. Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
  7. Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
  8. Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
  9. Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
  10. The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
  11. Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
  12. Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
  13. Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
  14. Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
  15. Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
  16. Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
  17. Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
  18. Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
  19. Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
  20. Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
  21. Research on the variation in the Shields curve of silt initiation
  22. Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
  23. Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
  24. Analysis of the formation process of a natural fertilizer in the loess area
  25. Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
  26. Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
  27. Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
  28. Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
  29. Estimating Bowen ratio in local environment based on satellite imagery
  30. 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
  31. Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
  32. Study on the mechanism of plant root influence on soil properties in expansive soil areas
  33. Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
  34. Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
  35. Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
  36. Predicting coastal variations in non-storm conditions with machine learning
  37. Cross-dimensional adaptivity research on a 3D earth observation data cube model
  38. Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
  39. Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
  40. Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
  41. Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
  42. Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
  43. Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
  44. Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
  45. Identification of radial drainage networks based on topographic and geometric features
  46. Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
  47. Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
  48. Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
  49. Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
  50. Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
  51. An efficient network for object detection in scale-imbalanced remote sensing images
  52. Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
  53. Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
  54. A modified Hoek–Brown model considering softening effects and its applications
  55. Evaluation of engineering properties of soil for sustainable urban development
  56. The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
  57. Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
  58. Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
  59. Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
  60. 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
  61. Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
  62. Land use classification through fusion of remote sensing images and multi-source data
  63. Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
  64. Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
  65. Factors impacting spatial distribution of black and odorous water bodies in Hebei
  66. Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
  67. Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
  68. Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
  69. Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
  70. Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
  71. Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
  72. Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
  73. Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
  74. Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
  75. Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
  76. Estimating the travel distance of channelized rock avalanches using genetic programming method
  77. Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
  78. New age constraints of the LGM onset in the Bohemian Forest – Central Europe
  79. Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
  80. Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
  81. Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
  82. Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
  83. Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
  84. Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
  85. Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
  86. Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
  87. Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
  88. Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
  89. A test site case study on the long-term behavior of geotextile tubes
  90. An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
  91. Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
  92. Comparative effects of olivine and sand on KOH-treated clayey soil
  93. YOLO-MC: An algorithm for early forest fire recognition based on drone image
  94. Earthquake building damage classification based on full suite of Sentinel-1 features
  95. Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
  96. Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
  97. An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
  98. Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
  99. Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
  100. Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
  101. A metaverse-based visual analysis approach for 3D reservoir models
  102. Review Articles
  103. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  104. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  105. Ore-controlling structures of granite-related uranium deposits in South China: A review
  106. Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
  107. 10.1515/geo-2025-0861
  108. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
  109. Depopulation in the Visok micro-region: Toward demographic and economic revitalization
  110. Special Issue: Geospatial and Environmental Dynamics - Part II
  111. Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
  112. Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
  113. Minerals for the green agenda, implications, stalemates, and alternatives
  114. Spatiotemporal water quality analysis of Vrana Lake, Croatia
  115. Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
  116. Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
  117. Regional patterns in cause-specific mortality in Montenegro, 1991–2019
  118. Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
  119. Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
  120. Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
  121. Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
  122. Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
  123. Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
  124. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 18.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/geo-2025-0824/html
Scroll to top button