Startseite Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China
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Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China

  • Yuting Dai EMAIL logo , Yangyang Zhao , Lintao Luo , Yafei Ji und Jian Wang
Veröffentlicht/Copyright: 21. November 2025
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Aus der Zeitschrift Open Geosciences Band 17 Heft 1

Abstract

Although northwest China faces challenges in achieving carbon neutrality, it has the potential for ecological carbon sequestration. Based on panel data from five northwestern Chinese provinces from 2003 to 2023, this study used an SBM-data envelopment analysis model to evaluate agricultural ecological efficiency (AEE) and analysed the spatial evolution and driving factors using ArcGIS and the Moran index. The results show that: (1) AEE shows a fluctuating upward trend with significant differences between provinces, with Shaanxi performing best and Ningxia performing worst; (2) the evolution of AEE can be divided into three stages: The early stage (2003–2011) was dominated by input factors; the middle stage (2012–2017) was influenced by ecosystem services; and the late stage (2018–2023) was the key constraint. Rural electricity consumption, agricultural diesel consumption, and the effective irrigation area were the core driving factors. The expected output affected the spatial pattern through a synergistic effect. Suggestions: (1) Establish an inter-provincial cooperation mechanism to narrow the regional gap through ecological compensation and technology transfer. Promote water-saving irrigation and organic agriculture in low-efficiency areas (e.g. Ningxia and Qinghai) and build green technology demonstration bases in high-efficiency areas (e.g. Shaanxi and Gansu). (2) Promote the green transformation of agricultural energy. Promote solar irrigation and electric agricultural machinery through subsidy policies. Develop intelligent irrigation technology using the Internet of Things. Reduce diesel dependence and improve the efficiency of water resources. This research provides a scientific basis for optimising agriculture and achieving sustainable development in north-west China under the goal of carbon neutrality.

Keywords: Northwest China; agricultural ecological efficiency; factor detection analysis

1 Introduction

The People’s Republic of China attaches great importance to agriculture, regarding it as a stabilising factor within its economic policy. Historically, the country’s agriculture has been subject to what has been termed “oil agriculture,” with the objective of enhancing efficiency and productivity. This has resulted in a substantial investment in chemical fertilisers, pesticides, agricultural machinery, and other resources. Nevertheless, this has also resulted in a considerable degree of agricultural pollution. The incorporation of agricultural practices that enhance soil carbon is a salient feature of domestic climate strategies [1]. The Second National Soil Pollution Source Census Bulletin [2] indicates that agricultural sources are a significant contributor to the discharge of water pollutants. In 2017, emissions of chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus from agricultural pollutant sources accounted for 49.77, 22.42, 46.52, and 67.21%, respectively, of their respective discharges. The sixth assessment report of the Intergovernmental Panel on Climate Change (IPCC AR6) on agriculture states that global warming has impeded crop yield growth, that increased surface O3 concentrations have reduced crop yields, and that methane (CH4) emissions have exacerbated this adverse effect [3]. China is currently undergoing a transition period in its agricultural development. This transition is characterised by a shift in focus from the traditional emphasis on agricultural output [4] to a new focus on the development of eco-agriculture, resource-saving, and environmentally friendly agriculture. Consequently, in the new phase of agricultural development, which is moving away from a production-oriented approach towards one that prioritises quality enhancement, it is crucial to address the question of how to achieve green eco-agriculture and transform agricultural development into a high-quality, sustainable model. This is a pressing issue in the current context of agro-ecological development.

While agricultural ecological efficiency (AEE) measures the ratio of economic outputs to environmental inputs, ecological footprint (EF) theory [5] provides a complementary perspective by quantifying the bio-capacity demand of agricultural activities. In northwest China, where ecosystems are fragile and water resources are scarce, overexploitation of cropland and pastureland has resulted in a regional EF that exceeds local bio-capacity [6]. This imbalance highlights the urgency of improving coordination between AEE and absolute resource constraints. By analysing AEE spatial and temporal patterns and combining them with EF dynamics, this study determines whether efficiency gains actually reduce ecosystem pressures or merely delay resource depletion – a key question for achieving carbon neutrality.

The concept of AEE, which serves as a crucial indicator for assessing the efficiency of agricultural production and its impact on the environment, has garnered significant attention from scholars both within and beyond the academic realm. The notion of AEE has emerged as a pivotal concept within the broader discourse on eco-efficiency. In 1990, Schaltegger [7], proposed the concept of eco-efficiency, defined as the ratio of increased economic value to increased environmental impacts. In 1992, the World Business Council for Sustainable Development expanded the conceptualisation of eco-efficiency by defining it as “the production of competitively priced goods or services that satisfy human needs and improve the quality of life, while progressively reducing the ecological impacts and resource intensity of the life cycle of the goods or services.” Subsequently, scholars have introduced the concept of eco-efficiency into the field of agriculture, proposing the term “AEE.” This milestone is indicative of the attainment of environmentally sustainable and low-carbon agricultural development, while concurrently achieving agricultural production objectives [8,9]. A number of current studies are being conducted on the subject of AEE, including the following:

1.1 Measurement of AEE

The principal methods of measuring AEE include Data Envelopment Analysis (DEA), Stochastic Frontier Analysis, the EF Method, and Energy Value Analysis, amongst others [10,11]. The DEA method is the primary approach for measuring AEE, as it does not require the presetting of specific functional forms and can be used to assess the relative efficiency of multiple inputs and outputs [12,13,14]. In light of the aforementioned evidence, numerous scholars have devised an evaluation index system from the standpoint of inputs and outputs [15,16,17]. The input indexes are derived principally from fundamental production factors, including arable land, fertilisers, and the utilisation of agricultural films. Conversely, the output indexes are typically categorised into two distinct groups: desired outputs and non-desired outputs [18,19,20,21,22].

1.2 Spatial and temporal characteristics of AEE

Currently, the majority of scholars focus their research on the regional disparities and spatio-temporal differentiation characteristics of AEE, amongst other topics. The subjects of their studies encompass various administrative levels, including national, provincial, city, and county [23,24,25,26,27,28]. Chen et al. proposed an index system comprising agricultural economic growth, agro-ecological environmental protection, and resource conservation. He proceeded to measure and analyse China’s AEE, thereby identifying the eastern region as exhibiting the highest efficiency value [29]; Yang investigated the spatial and temporal characteristics and influencing factors of the AEE of Inner Mongolia. To this end, the researcher selected 12 leagues and municipalities in Inner Mongolia as the research object. The author posits a course of action for enhancing AEE and proffers suggestions in this regard [30]. Zhang et al.’s research focused on the spatial heterogeneity of AEE and the factors that influence it, with the Hunan County administrative unit serving as the primary research subject. The findings indicate that the AEE of Hunan County demonstrates an upward trend, characterised by fluctuations [31].

Additionally, some studies encompass regions such as urban agglomerations and economic zones [32,33,34,35]. In order to assess the eco-efficiency of the three major economic zones of Shandong Province in the context of dual carbon, Zhang and Gao employed the SBM model, which revealed significant regional heterogeneity [36]. Furthermore, scholars engaged in international studies contribute to this field of research. Zhang and Chen conducted a comprehensive analysis of AEE, comparing China’s performance with that of other countries using data from 68 countries from 1992 to 2008 [37]. The objective of this study is to propose a strategy for enhancing AEE in China. The findings indicate that enhancing land use efficiency and upgrading farmers’ skills can effectively achieve the desired improvement in AEE.

1.3 AEE influencing factors

The investigation of the factors influencing agricultural efficiency has been undertaken previously in other research contexts. The prevailing research trajectory is predominantly focused on the technical efficiency of the farm and the farm’s size. The findings indicate that farm size is a pivotal determinant of farm production efficiency. Furthermore, a substantial corpus of academic literature posits that agricultural policy exerts a profound influence on the trajectory and guidance of agricultural production. The construction of the indicator system for the factors influencing the AEE in China is mainly divided into two levels: the macro level and the micro level. At the macro level, the factors to be considered include agricultural policy and industrial structure, amongst others. In contrast, the micro level is characterised by the presence of specific items, including but not limited to agricultural fertilisers, pesticides, and films. Furthermore, certain scholars have proposed the inclusion of indicators for agricultural electricity consumption and the effective irrigated area, in consideration of the actual situation [38,39,40].

Northwest China constitutes an integral component of China’s agricultural landscape, exemplifying a region typified by elevated input, output, and diminished efficiency in agricultural operations. In this article, the SBM-DEA and spatial autocorrelation analysis are employed to investigate the spatial and temporal characteristics of AEE in Northwest China from 2003 to 2023 in the context of the dual-carbon strategy objective. In order to enhance the focus on the eco-efficiency of agriculture, this study employs the “agricultural net carbon sink” as a key output indicator to assess the impact of agricultural emission reduction and carbon sequestration. The research scope encompasses provinces and cities, to gain a more comprehensive understanding of the spatial and temporal variations in AEE across different research scales, and to ensure the scientific rigor and reliability of the study’s findings.

2 Overview of the study area

The Northwest region constitutes approximately one-third of China’s total land area and is the most underdeveloped region in the western part of the country. The country has considerable potential for the exploitation of its natural resources [41]. The research subject of this paper is a comprehensive examination of the geographical areas of Shaanxi Province, Gansu Province, Qinghai Province, Ningxia Hui Autonomous Region, and Xinjiang Uygur Autonomous Region, as shown in Figure 1, which collectively encompass an area of approximately 4.06 million km² [42]. Northwest China is located in the remote interior of the country, comprising a vast and sparsely populated region. The region is distinguished by its extensive deserts, including the Gobi Desert, and mountainous terrain, with limited flatlands. The region is susceptible to soil erosion, particularly in the forms of sandification and desertification, which have resulted in the creation of an environment that is highly vulnerable to ecological disruption.

Figure 1 Location of the study area.
Figure 1

Location of the study area.

As demonstrated in the China Statistical Yearbook and the China Rural Statistical Yearbook (Table 1), the total sown area of crops in Northwest China constitutes 9.73% of the country’s total. However, the grain output accounts for a mere 7.38% of the country’s total grain output and is responsible for 10% of the country’s consumption. The majority of the country’s fertiliser, agricultural film, rural electricity, diesel oil, and agricultural machinery power is concentrated in the north-western region. Moreover, the region accounts for a significant proportion of the country’s agricultural workforce. This equates to 8.35% of the total power of agricultural machinery and 10.81% of the number of employees in the planting industry. The data presented above illustrate that the agricultural production mode in Northwest China remains characterised by a high input, high output, and low efficiency ratio. Furthermore, the utilisation rate of chemical fertilisers and pesticides in the production process is comparatively low, which has resulted in significant endogenous pollution.

Table 1

Agricultural inputs and outputs in the Northwest vs the Nation in 2023

Index Northwest China China Percentage
Crop planting area (ha) 16542.9 169991.0 9.7
Effective irrigation area (ha) 9828.4 70358.9 13.9
Plantation employees (×104 person) 1910.0 17663.0 10.8
Rural power consumption (×108 kW h) 532.4 6618.6 8.0
Fertiliser usage (×104 t) 556.6 5079.2 10.9
Pesticide usage (×104 t) 6.1 119.0 5.1
Agricultural film usage (×104 t) 51.21 237.5 21.5
Agricultural diesel usage (×104 t) 241.9 1769.0 13.6
Agricultural machinery power (×104 kW h) 9232.7 110597.2 8.3
Grain production (×104 t) 4859.5 68652.8 7.1
Crop planting area (ha) 16542.9 169991.0 9.7

In recent years, there has been a reiteration on numerous occasions within our country of the necessity of developing an ecological civilisation. This has encompassed deliberations on strategies to achieve the conservation of agricultural inputs, the realisation of efficient agricultural practices, the promotion of sustainability in agriculture, and the reduction of agricultural inputs. The most significant challenges currently being confronted by Northwest China pertain to enhancing output efficiency and the promotion of environmentally sustainable agricultural practices. It is therefore of great practical significance for the theory and practice of agricultural production in the Northwest to improve the efficient use of agricultural resources based on AEE, with the aim of promoting the sustainable development of agriculture by weakening the agro-ecosystem and reducing environmental pollution at its source.

3 Research methodology and data sources

3.1 Construction of the evaluation index system

At present, research in broad agriculture (comprising agriculture, forestry, animal husbandry, and fishery) necessitates the monitoring of a greater number of input and output indicators. This results in a lower level of accuracy in the evaluation of efficiency. Conversely, the narrower interpretation of agriculture, namely the plantation industry, exhibits a superior degree of precision in the assessment of AEE in comparison to the broader interpretation of agriculture. The plantation industry is the most prominent sector of agriculture in Northwest China, serving as a pivotal driver for the sustainable and high-quality advancement of agriculture in the region. The present study thus focuses on the plantation industry in a narrow sense as the subject of investigation. To this end, the study employs a 20-year period from 2003 to 2023 to assess the AEE of Northwest China. The timeframe selected for this study was determined by a careful consideration of the practicalities involved in agricultural production and the availability of relevant data. The selected input indicators encompass land, water resources, labour, energy, fertiliser, pesticide, agricultural film, and machinery.

Of these, labour and land represent the fundamental inputs to agricultural production. The number of people employed in agriculture is expressed in terms of labour input, while the total sown area of crops is expressed in terms of land input. The water input to agricultural production is measured by the effective irrigated area, and pesticides, agricultural films, and fertilisers are the main inputs to agricultural production. The amount of pesticides used, the amount of agricultural films used, and the amount of chemical fertilisers applied are used to express these inputs. The necessary resource inputs for “petroleum agriculture” are mechanical power and energy, which are expressed in terms of the total power of agricultural mechanisation and the amount of agricultural diesel used. The selection of economic output and ecological output as the desired output indicators is made, with economic output expressed as gross agricultural output value and ecological output expressed as net agricultural carbon sink. The selection of agricultural carbon emissions and surface pollution emissions as non-desired output indicators was made on the basis of the following evidence. The specific indicator system is presented in Table 2.

Table 2

AEE indicator system

Primary indicators Secondary indicators Variables
Factor inputs Land inputs Crop planting area (ha)
Water input Effective irrigation area (ha)
Labour input Plantation employees (×104 person)
Energy input Rural power consumption (×108 kW h)
Fertiliser input Fertiliser usage (×104 t)
Pesticide input Pesticide usage (×104 t)
Agricultural film input Agricultural film usage (×104 t)
Machinery input Agricultural machinery power (×104 kW h)
Desired output Economic output Grain production (×104 t)
Ecological output Agricultural net carbon sink (×104 t)
Non-desired output Agricultural carbon emissions Calculated based on the coefficient method (t)
Agricultural non-point source pollution Chemical fertilisers, pesticides, agricultural film, livestock, and poultry breeding pollution emission combination (t)

3.2 Data sources

The input indicators comprise the total sown area of crops, the effective irrigated area, the number of employees in the plantation industry, rural electricity consumption, the amount of chemical fertiliser used, the amount of pesticide used, the amount of agricultural film used, and the total power of agricultural machinery. These data have been extracted from the China Statistical Yearbook and the China Rural Statistical Yearbook for the period 2003–2023.

The desired output, namely grain output, is derived from the China Statistical Yearbook for the period 2003–2023. The undesired output is then calculated.

3.3 Methodology for measuring AEE

3.3.1 DEA

DEA is a multi-input and multi-output analysis method that was developed by a group of scholars. It is consistent with the ecological efficiency principles observed in the Northwest Chinese context. It is capable of evaluating the relative effectiveness of the same factors. In order to further consider the issue of unintended outputs, this article employs the SBM-DEA model, an efficiency evaluation model based on non-radial and non-angular approaches [43,44].

Let us suppose that the evaluation system comprises n decision-making units, each of which has an input–output vector. This vector comprises three elements with equation (1): an input vector, an expected output vector, and an unexpected output vector. The input vector, expected output vector, and unexpected output vector are, respectively, x ∈ R m , y g ∈ R s 1 , y b ∈ R s 2 , can be defined into three matrices X, Y g , Y b

(1) X = [ x 1 , x 2 … x n ] ∈ R m × n Y g = [ y 1 g , y 2 g … y n g ] ∈ R s 1 × n Y b = [ y 1 b , y 2 b … y n b ] ∈ R s 2 × n ,

where X > 0, Y g > 0, Y b > 0.Then, the production possibilities of the system are determined using equation (2).

(2) P = { ( x , y g , y b ) | x ≥ X λ , y g ≥ y g λ , y b ≥ y b λ , λ ≥ 0 } min ρ * = 1 − 1 m ∑ i = 1 m s i x i 0 1 + 1 s 1 + s 2 ∑ r = 1 s 1 s r g y r 0 g + ∑ r = 2 s 2 s r b y r 0 b x 0 = X λ + s − y 0 g = y g λ − s g y 0 b = y b λ + s b s − ≥ 0 , s g ≥ 0 , s b ≥ 0 ,

where s represents the slack variable of input–output, λ represents the weight vector, and ρ * represents the efficiency, ρ * ∈ [ 0,1 ] , ρ * = 1 , which means that the decision-making unit is completely effective, s − = s g = s b = 0 ; ρ * < 1 , it means that there is an efficiency loss in the efficiency of the decision-making unit. We can improve efficiency by optimising the input–output relationship.

3.3.2 Spatial analysis method

The concept of global spatial autocorrelation is used to describe the characteristics of a specific attribute in the context of global space. This method can be employed to ascertain whether AEE is spatially dependent, thus elucidating the spatial aggregation law of AEE. In most cases, the Global Moran’s I is calculated. The Global Moran’s I is a statistic that falls within the range of −1 to 1. A value greater than zero indicates the presence of either clustered high-value areas or clustered low-value areas, which is indicative of a positive spatial correlation. A positive spatial correlation is indicated by a larger value, whereas a smaller value indicates a negative spatial correlation. In the latter case, the spatial difference is smaller. When the value is less than 0, it signifies that high-value and low-value areas are clustered, which is indicative of a negative spatial correlation. The smaller the value, the greater the spatial difference. A value of 0 indicates that the significance test has not been passed and that there is no spatial correlation. Local spatial autocorrelation is a statistical technique that assesses the dissimilarity and statistical significance between attribute units and their neighbouring units. The calculation is typically performed using Local Moran’s I, which can be divided into five spatial association patterns: high-high type (HH), high-low type (HL), low-high type (LH), low-low type (LL), and non-significant type (N). Types HH and LL indicate a comparable concentrated distribution of high and low values, respectively, exhibiting spatial positive correlation characteristics. In contrast, types HL and LH indicate that high-value areas are surrounded by low-value areas and low-value areas are surrounded by high-value areas, demonstrating spatial negative correlation. The designation “Type N” indicates that the level of spatial aggregation is not high [45,46].

3.3.3 Standard deviation ellipse

The standard deviation ellipse was initially proposed by Welty Lifeffer in 1926 and can be employed to quantify the direction and distribution trend of attributes. The expression for the Centre of the ellipse is given by equations (3) and (4)

(3) SDE x = ∑ i = 1 n ( x i − X ̅ ) n SDE y = ∑ i = 1 n ( y i − Y ̅ ) n ,

where x i and y i are the spatial position coordinates of each feature, X ̅ and Y ̅ are the arithmetic mean centres, and SDE x and SDE y are the centres of the ellipse

(4) δ x = 2 ∑ i = 1 n ( x i ̅ cos θ − y i ̅ sin θ ) 2 n δ y = 2 ∑ i = 1 n ( x i ̅ cos θ + y i ̅ sin θ ) 2 n ,

where θ , δ x , δ y are the corners of the ellipse, the length of the ellipse on the X axis, and the length of the ellipse on the Y axis.

In the context of statistical analysis, the major half axis of an ellipse is indicative of the direction of data distribution, whereas the minor half axis represents the range of data distribution. As the discrepancy between the values of the major and minor axes increases (and thus the flatness of the data set becomes more pronounced), the directionality of the data becomes more evident. Conversely, if the discrepancy is less pronounced, the directionality is less discernible. The shorter the major and minor axes, the greater the concentration of the data. Conversely, an increase in the length of the major and minor axes results in a greater dispersion of the data.

4 Results

4.1 Carbon emissions calculation

The primary sources of agricultural carbon emissions in the northwestern region of China are the emissions generated by cultivated land ecosystems and those produced by animal husbandry and breeding industries. Carbon emissions from cultivated land ecosystems are primarily the result of resource consumption during agricultural production. This consumption can be attributed to six main aspects. As illustrated in Table 3, the calculation of agricultural carbon emissions is based on the coefficient method and takes into account the six aforementioned

(5) C ie = ∑ i − 1 n I i × C i .

In equation (5), C i e represents the carbon emissions generated when agricultural production resources are invested, while I i denotes the specific investment resource in question. Finally, C i signifies the carbon emission factor associated with a given category of agricultural resources [47,48] and Table 3.

Table 3

Emission factor

Carbon source Emission factor Cardinal number Source
Pesticide 4.931 kg(CO2eq)/kg Pesticide usage Oak Ridge National Laboratory, USA [49,50]
Fertiliser 0.8956 kg(CO2eq)/kg Fertiliser usage West and Marland [51]
Agricultural film 5.1800 kg(CO2eq)/kg Agricultural film usage Intergovernmental Panel on Climate Change
Irrigation 20.4760 kg(CO2eq)/kg Effective irrigation area Rural Development Research Center of Hubei
Plough 3.1260 kg(CO2eq)/kg Crop planting area College of Agronomy and Biotechnology, China Agricultural University
Agricultural machinery 0.5927 kg(CO2eq)/kg Agricultural diesel usage Intergovernmental Panel on Climate Change

The field of animal husbandry in the northwestern region of China is undergoing a period of rapid development. Animal husbandry constitutes a significant component of agricultural production and is a major contributor to overall carbon emissions. This article presents the calculation of emission factors corresponding to each species in accordance with the IPCC and China’s Guidelines for the Compilation of Provincial Greenhouse Gas Inventories (Trial). Equation (6) is provided below

(6) E c = ∑ ( N n × X n ) = ∑ M n × DAYs_alive n 365 × ( α n + β n + γ n ) N n = M n × DAYs_alive n 365 ,

where E c represents the carbon emission level of animal husbandry; N n represents the number of improvements of various types of livestock and poultry; M n represents the number of livestock and poultry at the end of the year; X n represents the total carbon emission coefficient of various types of livestock and poultry; α n, β n, γ n represent the carbon emission coefficient of CH4 emissions of various types of livestock and poultry, the carbon emission coefficient of CH4 emissions from manure processing, and the carbon emission coefficient of N2O emissions, respectively.

4.2 Carbon sink calculation

The majority of agricultural carbon sinks can be attributed to the biomass carbon sinks stored by crops through the process of photosynthesis. The estimation of carbon sinks is dependent upon a number of factors, including the yield, carbon absorption rate, economic coefficient, and water content of the crops in question. As illustrated in Table 4, the estimation model is as follows equation (7):

(7) C I = ∑ i = 1 k C i × ( 1 − W i ) × D i = ∑ i = 1 k C i × ( 1 − W i ) × Y i H i

Table 4

Basic data

Crops Carbon absorption rate Economic coefficient Water content
Rice 41 49 15
Wheat 49 43 13
Corn 47 44 14
Potato 42 67 55
Beans 45 39 18
Tobacco leaves 45 83 17
Vegetables 45 83 85

In this context, C I represents the total crop carbon sink, while i denotes a specific crop. The variable k refers to the type of crop. The term C i signifies the carbon sequestration rate of crop type i, while W i denotes the average water content of crop type i. The variable D i represents the biological yield of crop type i, and Y i denotes the economic yield of crop type i. Finally, the term H i denotes the economic coefficient of crop type i.

4.3 Analysis on the change process of agricultural eco-efficiency

This study demonstrates that the AEE in Northwest China exhibits a wave-like pattern of fluctuating growth and decline from 2003 to 2023. There are temporal variations in the AEE of each province. The data indicate that the overall performance is as follows: Shaanxi, Gansu, Xinjiang, Qinghai, and Ningxia. These findings are illustrated in Table 5 and Figure 2. In 2010, all provinces exhibited a consistent trajectory of growth. Of these, Shaanxi Province demonstrated the most pronounced improvement in ecological efficiency, with an increase of 1.27. From 2021 to 2023, the AEE of all provinces is expected to demonstrate a notable increase, with the Ningxia Hui Autonomous Region exhibiting the most pronounced improvement.

Table 5

2003–2023 Changes in AEE in Northwest China

Areas 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023
Shaanxi Province 0.37 0.46 0.51 0.58 0.69 0.84 0.68 0.75 0.87 0.78 1.00
Gansu Province 0.28 0.39 0.32 0.33 0.36 0.41 0.45 0.48 0.56 0.57 0.93
Qinghai Province 0.15 0.20 0.21 0.22 0.22 0.26 0.33 0.38 0.42 0.52 1.00
Ningxia Hui Autonomous Region 0.20 0.22 0.23 0.25 0.25 0.28 0.31 0.32 0.35 0.41 0.78
Xinjiang Uygur Autonomous Region 0.17 0.19 0.21 0.26 0.29 0.32 0.38 0.41 0.46 0.47 0.72
Figure 2 Curves of change in indicators: (a) Rural power consumption, (b) fertiliser usage, (c) crop planting area, (d) agricultural film usage, (e) agricultural diesel usage, (f) plantation employees, (g) pesticide usage, (h) effective irrigation area, (i) grain production, (j) agricultural machinery power, (k) agricultural carbon emissions.
Figure 2 Curves of change in indicators: (a) Rural power consumption, (b) fertiliser usage, (c) crop planting area, (d) agricultural film usage, (e) agricultural diesel usage, (f) plantation employees, (g) pesticide usage, (h) effective irrigation area, (i) grain production, (j) agricultural machinery power, (k) agricultural carbon emissions.
Figure 2

Curves of change in indicators: (a) Rural power consumption, (b) fertiliser usage, (c) crop planting area, (d) agricultural film usage, (e) agricultural diesel usage, (f) plantation employees, (g) pesticide usage, (h) effective irrigation area, (i) grain production, (j) agricultural machinery power, (k) agricultural carbon emissions.

A comparison of the input–output figures for agricultural production in the Northwest Territories reveals an inverted U-shaped trajectory in the utilisation of chemical fertilisers, agricultural films and pesticides from 2003 to 2023. Grain production exhibited a fluctuating upward trend, with a sudden decline in 2015 and a severe drought in the north-western region, which impacted the stability of grain production and yields. Furthermore, the ecosystem service functions exhibited a distinctive pattern of change, characterised by a “rising–declining–raising” trajectory. The data indicate an upward trend in agricultural carbon emissions, with a particularly pronounced increase between 2012 and 2017. This growth can be attributed to the extensive use of chemical fertilisers, pesticides, and agricultural films during this period. Nevertheless, agricultural carbon emissions have since declined, indicating the efficacy of a series of policy measures and special actions implemented to stabilise food production in China. The implementation of the “one-control-two-reduce-three-basic” policy in 2015 has been a substantial contributing factor. In 2015, China promulgated the “One Control, Two Reductions, Three Fundamentals” policy, which sets forth measures for the reduction of emissions and the sequestration of carbon, including the conservation of agricultural water, the cessation of the growth of chemical fertilisers, and the comprehensive utilisation of agricultural waste, amongst other strategies. In addition, a substantial financial investment is made on an annual basis to support the advancement of sophisticated agricultural land. This investment has the potential to not only promote the adoption of innovative techniques, such as water-saving irrigation, but also contribute to the mitigation of carbon emissions. This has the potential to engender a relationship characterised by mutual reinforcement between enhanced food production and reduced carbon emissions. With regard to inputs, the exception being the number of people employed in the plantation industry, which has exhibited a year-on-year decline, other indicators have demonstrated varying degrees of growth.

The findings indicate that the preceding period (2003–2011) demonstrated a predominantly input-driven AEE. The substantial endowment of natural resources in the Northwest Region, coupled with a relatively sparse population, necessitated an augmentation in factor inputs to achieve a substantial increase in production capacity. This, in turn, resulted in a considerable enhancement in AEE. The medium-term changes in AEE (2012–2017) are primarily influenced by ecosystem service function. The enhancement in agricultural production levels has resulted in a corresponding rise in resource consumption and pollution, leading to adverse effects on the ecological environment. This has consequently resulted in a decline in the ecosystem service function of Shaanxi Province and a subsequent reduction in AEE. In the subsequent period (2018–2023), the growth of AEE is closely related to the reduction of non-desired output. In response to this challenge, the People’s Republic of China has adopted a series of policy measures and special actions with the objective of stabilising food production. The management of agricultural pollution emissions has been a priority in Shaanxi Province, Xinjiang Uygur Autonomous Region, and Gansu Province. Consequently, agricultural carbon emissions have been reduced on an annual basis, agriculture has entered a green development stage, and AEE has been steadily improved.

4.4 Analysis on the change rule of AEE

As demonstrated in Table 6, the AEE can be categorised into five distinct classes. From 2003 to 2023, the area of high value of the AEE in Northwest China will undergo a gradual decline, signifying that the AEE in Northwest China possesses considerable potential for enhancement. As demonstrated in Figures 3 and 4, the North West Region’s AEE exhibits a spatial pattern of “high in the east and low in the west.” As time progresses, there is an observable increase in the high-value areas, with a concomitant tendency for the distribution area to expand in an eastward direction. The low-value areas are concentrated and contiguous, primarily distributed in the western region, including Qinghai Province, the Xinjiang Uygur Autonomous Region, and Ningxia Hui Autonomous Region. The findings indicate a close correlation between AEE and both resource background conditions and ecological environment. From 2003 to 2008, there was an increase in the number of high-value units, which were primarily concentrated in the southern region of Shaanxi. This was primarily due to the region’s abundant water resources, which contributed to the overall ecological environment being favourable, and the presence of abundant natural resources.

Table 6

Classification of agricultural efficiency levels

Levels Low Medium Medium to high level High DEA efficient
Agricultural efficiency (0, 0.4) (0.4, 0.6) (0.6, 0.8) (0.8, 1) 1
Figure 3 Spatial patterns of agriculture ecological efficiency in the north-west: (a) in 2003, (b) in 2008, (c) In 2013, (d) in 2018, (e) in 2023.
Figure 3

Spatial patterns of agriculture ecological efficiency in the north-west: (a) in 2003, (b) in 2008, (c) In 2013, (d) in 2018, (e) in 2023.

Figure 4 Global Moran’s I Index of AEE in the Northwest: (a) In 2003, (b) in 2008, (c) in 2013, (d) in 2018, (e) in 2023.
Figure 4

Global Moran’s I Index of AEE in the Northwest: (a) In 2003, (b) in 2008, (c) in 2013, (d) in 2018, (e) in 2023.

From 2008 to 2013, the ecological efficiency of Qinghai, Gansu, and other regions underwent an increase, with the primary focus of these efforts being the Haixi Mongolian and Tibetan Autonomous Prefecture, Jiayuguan City and Haibei Tibetan Autonomous Prefecture. Conversely, from 2013 to 2018, the ecological efficiency of Bayingoleng Mongolian Autonomous Prefecture and Guoluo Tibetan Autonomous Prefecture in Xinjiang demonstrated a downward trend. From 2018 to 2023, there will be a substantial enhancement in the ecological efficiency of Xinjiang, Qinghai, and other regions; a steady improvement will be observed in the ecological efficiency of Shaanxi, Gansu, Ningxia, and other areas; and the overall ecological efficiency of the Northwest region will undergo a significant enhancement.

4.5 Influencing factors of AEE

4.5.1 Factor detection analysis

Based on ArcGIS software to discretise the data, and then through geodetic detectors to further study the impact of the different types of indicators constituting AEE on the spatial differentiation of AEE, the results show (Table 7) rural electricity consumption, pesticide input, agricultural film input, fertiliser use, and agricultural carbon emissions are the dominant factors affecting the spatial differentiation of AEE in the Northwest region; the effective irrigation area, sown area, agricultural diesel consumption, and number of employees play an important role in AEE, while the total power of agricultural machinery and grain production have a weaker impact on AEE.

Table 7

Detection factors for indicators

Detection index 2003 2008 2013 2018 2023
Crop planting area (X1) q p q p q p q p q p
Effective irrigation area (X2) 0.12 0.29 0.16 0.22 0.18 0.34 0.07 0.76 0.15 0.32
Plantation employees (X3) 0.17 0.64 0.25 0.07 0.41 0.00 0.24 0.08 0.15 0.61
Rural power consumption (X4) 0.16 0.11 0.19 0.08 0.04 0.74 0.02 0.94 0.17 0.17
Fertiliser usage (X5) 0.32 0.00 0.32 0.00 0.26 0.02 0.20 0.07 0.17 0.42
Pesticide usage (X6) 0.28 0.02 0.16 0.10 0.12 0.32 0.04 0.82 0.08 0.56
Agricultural film usage (X7) 0.13 0.27 0.18 0.12 0.12 0.34 0.04 0.83 0.14 0.17
Agricultural machinery power (X8) 0.11 0.50 0.19 0.52 0.20 0.35 0.09 0.74 0.05 0.89
Agricultural diesel usage (X9) 0.11 0.50 0.14 0.27 0.10 0.39 0.04 0.78 0.11 0.54
Grain production (X10) 0.07 0.58 0.20 0.19 0.10 0.43 0.01 0.98 0.19 0.12
Agricultural carbon emissions (X11) 0.08 0.62 0.10 0.48 0.13 0.65 0.03 0.97 0.14 0.37

1q denotes the factor detection value of the detection indicator X i. The larger the value of q, the greater the effect of X i. on the spatial divergence of AEE.

The impact of different indicators on the spatial divergence of AEE varies over the period 2003–2023. The use of agricultural diesel has moved from last to first place, with a gradual increase in its impact on AEE, and the use of agricultural machinery is beginning to increase in the Northwest. Energy is the driving force behind agricultural production. The trend of rural electricity consumption in the Northwest Territories is increasing year by year, but its impact on AEE is decreasing year by year, indicating that the agricultural electricity infrastructure in the Northwest Territories is still relatively backward, and that farmers lack advanced agricultural equipment and technology and do not make full use of electricity resources. These agrochemicals have a lasting impact on AEE in the Northwest Territories, where excessive use of fertilisers, pesticides, and agricultural film can lead to ecosystem imbalances. Agricultural carbon emissions were the leading influence in 2003, 2008, and 2013, and their influence has decreased so far in 2018, suggesting that the carbon reduction measures implemented in recent years in the Northwest Territories have been effective in improving the stability and sustainability of the ecosystem. The impact of agricultural labour on AEE is variable and fluctuating, falling to its lowest level in 2018, with less impact on the spatial pattern of AEE at a time when the widespread use of modern agricultural technologies has reduced the dependence of agricultural production on labour.

4.5.2 Interactive probe analysis

The present study employs the geodetector’s interaction detection functionality to investigate the change rule of the influence on AEE when different indicators interact. As demonstrated in Figure 5, the q value of the interaction of each indicator exhibits varying degrees of increase from 2003 to 2023, thereby indicating that the influence on AEE will be strengthened when two factors work in conjunction. The primary interaction categories observed for the years 2003, 2008, 2013, 2018, and 2023 encompassed grain production and fertiliser application (q = 0.572), grain production and rural electricity utilisation (q = 0.648), rural electricity use and effective irrigated area (q = 0.674), agricultural diesel use and effective irrigated area (q = 0.604), and rural electricity use and effective irrigated area (q = 0.484). It is evident that rural electricity consumption, agricultural diesel use, and effective irrigated area not only have significant effects on factor detection, but also belong to the dominant factors in the interaction detection.

Figure 5 Interactive detection value: (a) In 2003, (b) in 2008, (c) in 2013, (d) in 2018, (e) in 2023.
Figure 5

Interactive detection value: (a) In 2003, (b) in 2008, (c) in 2013, (d) in 2018, (e) in 2023.

The Impact of grain yield and ecosystem service function on factor detection is negligible, yet their significance in interaction detection is substantial. This finding suggests that the desired output exerts a significant influence on the spatial pattern of AEE, predominantly through synergistic interactions with other factors. From 2003 to 2018, there was a discrepancy between the high-yielding areas in northwest China and the high-efficiency areas. This phenomenon can be attributed to the over-investment in production factors such as chemical fertilisers, pesticides, agricultural films, and machinery during this period, resulting in inefficiencies. The eco-efficiency of the high-yielding areas continued to improve after 2018. This can be attributed to the robust development of green agricultural policies, which effectively guarantee agroecology, thereby contributing to the enhancement of AEE.

5 Discussion

This study used the SBM-DEA model to evaluate AEE in northwestern China between 2003 and 2023. The analysis examined the spatio-temporal characteristics of AEE and identified the primary influencing factors using ArcGIS and the Geodetector model. The results of the AEE measurement indicate that AEE in northwestern China has fluctuated in a wave-like manner over time, with differences observed between provinces. This view is supported by existing research [29]. However, this article also found that Shaanxi Province performed better overall than Gansu Province and the Xinjiang Uygur Autonomous Region, followed by Qinghai Province and the Ningxia Hui Autonomous Region. In the initial stage (2003–2011), AEE was primarily influenced by the input side, exhibiting a significant increase. During the subsequent period (2012–2017), changes in AEE were primarily driven by the provision of ecosystem services. The continuous increase in agricultural output during this period led to ecological degradation, damage to ecosystem services and a decline in AEE in Shaanxi Province. The growth of AEE in the subsequent period (2018–2023) was closely related to poor production. Total AEE in north-west China exhibits a spatial pattern of “high in the east and low in the west.” Over time, the number of high-value areas increases and their distribution range expands eastwards, while low-value areas become more concentrated and continuous, mainly in the western region, including Qinghai Province, the Xinjiang Uyghur Autonomous Region, and the Ningxia Hui Autonomous Region.

From the perspective of influencing factors, rural electricity consumption, agricultural diesel oil use, and the area under effective irrigation are important not only in factor detection, but also in interaction detection. This is consistent with existing literature on this topic. Grain yield and ecosystem service function have little influence on factor detection; however, their roles in interaction detection are considered quite significant. These indicators differ from those in the existing literature, mainly due to the different indicators used. At the same time, this article considers the influence of the spatial pattern of AEE. The spatial pattern of AEE is primarily affected by expected output, and this influence is exacerbated by synergy with other factors.

6 Conclusions

In light of the preceding discourse, the subsequent policy recommendations are hereby proposed: (1) It is imperative to optimise the planting structure according to the varying climatic, soil, and other natural conditions in each region. The selection of crops suitable for the local ecological environment is pivotal for enhancing crop yield and quality while minimising the negative environmental impact. Furthermore, it is crucial to encourage agricultural planters to adopt ecological agricultural models, such as crop rotation, intercropping, and planting in sets, to enhance land utilisation efficiency and stabilise the ecosystem. The utilisation of the abundant light and heat resources in the northwestern region and the untapped Gobi resources presents a significant opportunity to develop facility agriculture. (2) The effective irrigated area in Northwest China exerts a significant influence on factor detection and is considered a primary factor in interaction detection. It is imperative that water resource management is strengthened. The establishment of a robust water resource management system is essential for effective control of the total amount of water used in agriculture. The implementation of quota management is crucial to ensure sustainable use of water resources. Furthermore, the promotion of efficient water-saving irrigation techniques, such as drip irrigation and sprinkler irrigation, is imperative to reduce water wastage and enhance the efficiency of irrigation water usage. The government has the capacity to provide financial subsidies and technical support. Furthermore, it can increase investment in farmland, water conservancy infrastructure construction, and improve the efficiency and coverage of irrigation systems. (3) The promotion of ecological agricultural models is imperative to reduce the use of chemical fertilisers and pesticides. This is because such practices can improve soil fertility and ecosystem diversity, while also encouraging agricultural growers to use biological pesticides and organic fertilisers. Such measures can reduce the pollution of the environment caused by chemical pesticides and fertilisers. (4) Furthermore, farmers should be encouraged to use protective ploughing techniques, which can reduce soil disturbance and bareness. This, in turn, can reduce soil erosion and carbon emissions, while also strengthening grassland ecological protection. (5) Finally, it is vital to improve the ecological function and production capacity of grasslands. The establishment of a robust regulatory framework for agro-ecology and the environment is imperative. This regulatory system should encompass the augmentation of regulatory oversight with regard to agricultural carbon emissions, the enhancement of monitoring and assessment practices within the agro-ecological and environmental domains, and the formulation of policies that address agro-ecological compensation. Furthermore, it is essential to allocate specific ecological compensation to those who adopt environmentally sustainable agricultural practices and contribute to the mitigation of environmental degradation. It is imperative that policy support is provided, including financial subsidies and tax incentives, with a view to encouraging farmers and enterprises to participate in agroecological protection and sustainable development.

The Northwest Region is located in the interior of China, where precipitation is low, evaporation is high, and water resources are scarce. Concurrently, the region exhibits a high degree of complexity in its topography and geomorphology, characterised by low soil organic matter content, nutrient deficiency, and severe salinisation. Northwest China constitutes a significant ecological barrier within China, with the ecological environment exerting a direct influence on the nation’s ecological security. The natural environment imposes constraints on agricultural development, yet with the rapid economic development and population growth witnessed in Northwest China, demand for agricultural products is increasing. This necessitates an improvement in the ecological efficiency of agriculture.

The present study explores the spatial and temporal differences in AEE and the dominant factors affecting AEE in Northwest China, with the aim of raising the importance of AEE for the government, farmers, and enterprises, and making targeted suggestions for improving AEE in Northwest China.

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  1. Funding information: This research was funded by Team of “scientists + engineers” for research on high-standard farmland under the “double carbon” goal and transformation of results (grant number 2023KXJ-160), Research on Intelligent Diagnosis and Improvement Decision making Based on Farmland Soil Quality (grant number 2024JC-YBMS-186).

  2. Author contributions: Yuting Dai: data analysis and writing. Yangyang Zhao: validation. Lintao Luo: investigation. Yafei Ji: data analysis and writing. Jian Wang: validation. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: The authors declare no conflicts of interest.

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Received: 2025-03-31
Revised: 2025-06-29
Accepted: 2025-08-01
Published Online: 2025-11-21

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

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

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  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. Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
  103. Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
  104. Study on the spatial equilibrium of cultural and tourism resources in Macao, China
  105. Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
  106. Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
  107. The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
  108. Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
  109. Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
  110. Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
  111. Comparison of several seismic active earth pressure calculation methods for retaining structures
  112. Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
  113. Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
  114. Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
  115. Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
  116. InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
  117. Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
  118. Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
  119. Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
  120. Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
  121. Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
  122. Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
  123. Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
  124. Stability analysis of valley-type upstream tailings dams using a 3D model
  125. Variation characteristics and attribution analysis of actual evaporation at monthly time scale from 1982 to 2019 in Jialing River Basin, China
  126. Investigating machine learning and statistical approaches for landslide susceptibility mapping in Minfeng County, Xinjiang
  127. Investigating spatiotemporal patterns for comprehensive accessibility of service facilities by location-based service data in Nanjing (2016–2022)
  128. A pre-treatment method for particle size analysis of fine-grained sedimentary rocks, Bohai Bay Basin, China
  129. Study on the formation mechanism of the hard-shell layer of liquefied silty soil
  130. Comprehensive analysis of agricultural CEE: Efficiency assessment, mechanism identification, and policy response – A case study of Anhui Province
  131. Simulation study on the damage and failure mechanism of the surrounding rock in sanded dolomite tunnels
  132. Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China
  133. Review Articles
  134. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  135. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  136. Ore-controlling structures of granite-related uranium deposits in South China: A review
  137. Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
  138. A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
  139. Advancements in machine learning applications for mineral prospecting and geophysical inversion: A review
  140. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
  141. Depopulation in the Visok micro-region: Toward demographic and economic revitalization
  142. Special Issue: Geospatial and Environmental Dynamics - Part II
  143. Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
  144. Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
  145. Minerals for the green agenda, implications, stalemates, and alternatives
  146. Spatiotemporal water quality analysis of Vrana Lake, Croatia
  147. Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
  148. Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
  149. Regional patterns in cause-specific mortality in Montenegro, 1991–2019
  150. Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
  151. Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
  152. Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
  153. Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
  154. Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
  155. Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
  156. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
  157. Complex multivariate water quality impact assessment on Krivaja River
  158. Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
  159. Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
  160. Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
  161. Flash flood potential index at national scale: Susceptibility assessment within catchments
  162. SWAT modelling and MCDM for spatial valuation in small hydropower planning
https://doi.org/10.1515/geo-2025-0872
Schlagwörter für diesen Artikel
Northwest China; agricultural ecological efficiency; factor detection analysis
Creative Commons
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Artikel in diesem Heft

  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. Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
  103. Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
  104. Study on the spatial equilibrium of cultural and tourism resources in Macao, China
  105. Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
  106. Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
  107. The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
  108. Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
  109. Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
  110. Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
  111. Comparison of several seismic active earth pressure calculation methods for retaining structures
  112. Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
  113. Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
  114. Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
  115. Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
  116. InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
  117. Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
  118. Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
  119. Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
  120. Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
  121. Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
  122. Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
  123. Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
  124. Stability analysis of valley-type upstream tailings dams using a 3D model
  125. Variation characteristics and attribution analysis of actual evaporation at monthly time scale from 1982 to 2019 in Jialing River Basin, China
  126. Investigating machine learning and statistical approaches for landslide susceptibility mapping in Minfeng County, Xinjiang
  127. Investigating spatiotemporal patterns for comprehensive accessibility of service facilities by location-based service data in Nanjing (2016–2022)
  128. A pre-treatment method for particle size analysis of fine-grained sedimentary rocks, Bohai Bay Basin, China
  129. Study on the formation mechanism of the hard-shell layer of liquefied silty soil
  130. Comprehensive analysis of agricultural CEE: Efficiency assessment, mechanism identification, and policy response – A case study of Anhui Province
  131. Simulation study on the damage and failure mechanism of the surrounding rock in sanded dolomite tunnels
  132. Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China
  133. Review Articles
  134. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  135. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  136. Ore-controlling structures of granite-related uranium deposits in South China: A review
  137. Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
  138. A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
  139. Advancements in machine learning applications for mineral prospecting and geophysical inversion: A review
  140. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
  141. Depopulation in the Visok micro-region: Toward demographic and economic revitalization
  142. Special Issue: Geospatial and Environmental Dynamics - Part II
  143. Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
  144. Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
  145. Minerals for the green agenda, implications, stalemates, and alternatives
  146. Spatiotemporal water quality analysis of Vrana Lake, Croatia
  147. Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
  148. Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
  149. Regional patterns in cause-specific mortality in Montenegro, 1991–2019
  150. Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
  151. Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
  152. Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
  153. Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
  154. Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
  155. Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
  156. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
  157. Complex multivariate water quality impact assessment on Krivaja River
  158. Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
  159. Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
  160. Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
  161. Flash flood potential index at national scale: Susceptibility assessment within catchments
  162. SWAT modelling and MCDM for spatial valuation in small hydropower planning
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Heruntergeladen am 24.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/geo-2025-0872/html
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