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Assessing the ecological security of tourism in Northeast China

  • Dan Shi , Jingwen Guan , Daiji Wan and Jiping Liu EMAIL logo
Published/Copyright: April 9, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

The ecological security of tourism provides an important guarantee of the sustainable development of regional tourism. In this study, the authors use the Driving Force-Pressure-State-Impact-Response model to construct a system to assess the ecological security of tourism in three provinces of Northeast China. We analyze the characteristics of the dynamic spatiotemporal evolution of the ecological security of tourism in 34 prefecture-level cities, one prefecture, and one region in three provinces of Northeast China from 2010 to 2019 and identify obstacles to this evolution. The results show the following: (1) The ecological security of tourism in Northeast China fluctuated and grew during the study period. Significant changes were observed in the rankings of the provinces in the ecological security of tourism. The pattern of spatial distribution has gradually shifted, from “high in the west and low in the middle” in 2010 to “slightly high in the middle-east and slightly low in the west” in 2019. (2) The ecological security of tourism exhibited prominent characteristics of spatial agglomeration on the whole, where the degree of agglomeration has gradually decreased over time. Economically developed cities in Central China exhibited strong local autocorrelation, and the majority had the characteristics of spatial agglomeration and dependence. (3) There were significant differences in obstacles to the ecological security of tourism among the cities (prefectures and regions) and provinces. Industrial sulfur dioxide emissions and urban population density are the main obstacles to the ecological security of tourism, while the pressure system has emerged as the main hindrance to improvements in it in the three provinces considered here. These results provide insights into the ecological security of tourism in the three provinces of Northeast China, as well as a theoretical reference for formulating and implementing the relevant measures of risk prevention and control. This study shows that increasing the protection of the tourism ecological environment and the construction of new energy sources, building complete tourism infrastructure, coordinating the relationship between tourists and the natural environment, adopting differentiated tourism development measures, and overcoming the obstacles will help to gradually improve the level of tourism ecological security in three provinces of Northeast China.

Keywords: tourism ecological security; dynamic spatiotemporal evolution; DPSIR model; fuzzy matter-element model; Northeast China

1 Introduction

Ecological security is an important part of the national security system and an important basis for realizing sustainable economic and social development. It has become an important component of China’s aim to construct an environmentally friendly society in recent years [1]. As it is one of the fastest growing industries in the country, the development of tourism promotes economic growth and social progress. Tourism has contributed to a certain extent to the degree of economic growth, and in the pursuit of faster development, it is constantly strengthening the development of tourism resources and the ecological environment. However, the construction and inevitable pollution from emissions caused by such development, as well as the damage sustained by the environment due to tourism-related activities, lead to a diversity of problems related to ecological security that weaken the capability of tourist destinations for sustainable development. Frequent human tourism activities inevitably result in varying degrees of environmental damage to tourist destinations. In addition, sometimes one-sided pursuit of rapid economic development leads to serious environmental problems. The tourism sector exhibits extensive development of tourist attractions and substantial environmental degradation, putting the tourism ecological environment to a significant test.

The ecological security of tourism is a composite system composed of three subsystems: society, economy, and environment. It is important for ensuring the ecological security of tourism [2]. The provinces of Heilongjiang, Liaoning, and Jilin in Northeast China are rich in tourism-related resources, including forests, grasslands, wetlands, and ice and snow, as well as the requisite industrial and agricultural resources. They are thus major tourist destinations in both summer and winter. However, owing to the rapid development of tourism, environmental problems with varying degrees of severity have arisen in most scenic spots in the region, affected the fragile tourism ecosystem, and hindered the sustainable development of regional tourism. We thus need to address the problems related to the ecological security of tourism in these three provinces, coordinate the development of regional tourism, protect the ecological environment, reasonably maintain and improve regional tourism ecological security, and achieve stable and sustainable development of the tourism industry.

A number of researchers have investigated the ecological security of tourism, and most relevant studies have focused on its basic connotations [3], theoretical systems for it [4], measurement and evaluation [5], dynamic mechanisms [6], spatial effects [7], forecasts [8], and early warning analyses [9]. The objects of research in the area have gradually changed from the macroscopic perspective of a given country [10], province [11], or city [12] to the microscopic perspective of nature reserves [13], scenic spots [14], wetlands [15], and islands [16]. Research on the ecological security of tourism remains in its exploratory stage, and few studies have investigated this in regional or urban agglomerations, especially in Northeast China. In order to enrich the research on tourism ecology in Northeast China, in this study, the authors consider 36 cities (prefectures and regions) in the aforementioned three provinces of Northeast China as the research sample. We construct a system of indices to assess the ecological security of tourism based on the Drive–Pressure–State–Influence–Response (DPSIR) model and introduce the fuzzy model of matter-element closeness to it for mathematical analysis. We then use time series analyses and the model of spatial autocorrelation analysis to explore characteristics of the spatiotemporal patterns of evolution of the ecological security of tourism in the three provinces and identify obstacles to it by using the grey correlation model. The aim is to improve research on the regional ecological security of tourism and to provide a basis for decision-making to ensure the sustainable development of tourism in the three provinces of Northeast China.

2 Overview of the study area and theoretical framework

2.1 Overview of the study area

The three northeastern provinces are located in northeastern China (Figure 1), neighboring Russia, North Korea, South Korea, Japan, and other countries, and serve as a gateway for China’s opening to the Northeast Asian region. These three provinces are Heilongjiang, Jilin, and Liaoning. Geographically, they are characterized by a landscape of mountains and water, predominantly experiencing a temperate monsoon climate. However, due to the high latitude of some areas, winters are cold and lengthy, while summers are warm but brief. The region experiences heavy snowfall during the winter months, with limited evaporation, resulting in a humid climate. The terrain of the three provinces of Northeast China is dominated by plains and mountains, which are materially rich, are China’s important production bases for timber and minerals, and harbor rich wildlife resources. The three provinces of Northeast China are vast and fertile, providing unique conditions for the development of agriculture, forestry, animal husbandry, and fisheries. As an important food production base in China, the region has made important contributions to supporting national construction and maintaining social stability. However, the rapid economic development in the early years of the northeastern region often overlooked the importance of environmental conservation. Pollutant emissions from tourism activities have caused certain impacts on the ecological security of tourist sites. The region faces significant challenges, marked by prominent human–environment conflicts and a severe decline in ecological functions, leading to the degradation of tourism resources and the environment, as well as posing a serious threat to biodiversity.

Figure 1 Geographical location map of the three provinces in Northeast China.
Figure 1

Geographical location map of the three provinces in Northeast China.

2.2 DPSIR evaluation index system

Most studies have used a combination of qualitative and quantitative spatiotemporal analyses. The theoretical frameworks developed to this end include the Environment-Economy-Society model [17], Pressure-State-Response model [18], DPSIR model [19], Analytic Hierarchy Process method [20], Markov chain [21], Technique for Order Preference by Similarity to an Ideal Solution method [22], ecological footprint [23], system dynamics model [24], data envelopment analysis model [25], minimum cumulative resistance model [26], and standard deviation-based ellipse model [27]. These techniques have enabled a systematic examination of the ecological security of tourism from the perspectives of geography, ecology, environment, tourism, and management.

The DPSIR model is composed of a “driving force” (D), “pressure” (P), “state” (S), “impact” (I), and “response” (R) [19–21]. This model can effectively depict the causal relationships within the system, encompassing elements such as tourism resources, the ecological environment, and human tourism activities. It serves to illustrate the threats posed by societal, economic, and human activities to tourism ecological security. Ultimately, it can demonstrate the feedback loop, whereby tourism activities result in impacts on the tourism ecological environment through the response (R) subsystem. It is used to express the coupling relationship between factors influencing the system of the ecological security of tourism. We apply this model to analyze the dynamic evolution of the ecological security of tourism in the three provinces of Northeast China and identify obstacles to it. We initially chose 40 evaluation indicators. To ensure the reasonableness of the system of indices, we referred to the indicators identified in the sustainable development goals (SDGs) [28,29], environmental performance index (EPI) [30,31], and past research [5,8,18–21,32–36]. Combining the current situation of tourism ecological security in three provinces of Northeast China with the SDGs and EPI indicators and using principal component analysis to synthesize the considerations, we finally identified 33 indicators (Table 1) and calculated their weights by using the entropy weight method ( ω j ).

Table 1

System of indices to evaluate the ecological security of tourism in Northeast China based on the DPSIR model

Criterion layer Element layer Index layer (units) Weight
Driving force (D) Tourism development Growth rate of tourism revenue (D₁) (%) 0.0311
Tourist growth rate (D₂) (%) 0.0311
Number of class A scenic spots (D₃) (piece) 0.0309
Economic development Economic development level (D₄) (million yuan) 0.0288
Per capita GDP level (D₅) (yuan) 0.0301
Civil life Natural population growth rate (D₆) (%) 0.0307
Urbanization rate (D₇) (%) 0.0307
Pressure (P) Tourism reception Tourist density (P₁) (people/km²) 0.0310
Tourism traffic pressure (P₂) (%) 0.0310
Civil life Urban population density (P₃) (people/km²) 0.0308
Per capita daily domestic water consumption (P₄) (tons) 0.0310
Ecological environment Smoke and dust emission quantity (P₅) (tons) 0.0311
Discharge quantity of industrial wastewater (P₆) (tons) 0.0312
Amount of industrial SO₂ emissions (P₇) (tons) 0.0312
State (S) Tourism economy Total tourism revenue (S₁) (million yuan) 0.0284
Per capita tourism income (S₂) (yuan) 0.0296
Civil life Per capita cultivated land area (S₃) (m²) 0.0288
Urban daily water supply capacity (S₄) (tons) 0.0287
Ecological environment Greening coverage rate of built-up area (S₅) (%) 0.0310
Per capita park green space area (S₆) (m²) 0.0302
Forest coverage (S₇) (%) 0.0302
Impact (I) Tourism economy Tourism economic density (I₁) (million yuan/km²) 0.0282
Proportion of tourism revenue in GDP (I₂) (%) 0.0291
Industrial economy Proportion of tertiary industry in GDP (I₃) (%) 0.0288
Increment rate of per capita consumption expenditure (I₄) (%) 0.0310
Growth rate of per capita disposable income (I₅) (%) 0.0312
Response (R) Environmental governance Proportion of days with excellent air quality (R₁) (%) 0.0308
Harmless treatment rate of domestic waste (R₂) (%) 0.0311
Centralized treatment rate of sewage treatment plant (R₃) (%) 0.0309
Comprehensive utilization rate of industrial solid waste (R₄) (%) 0.0309
Government regulation Proportion of energy conservation and environmental protection expenditure (R₅) (%) 0.0304
Number of employees in tourism industry (R₆) (people) 0.0311
Number of college students per 10,000 people (R₇) (people) 0.0287

3 Methodology and data sources

3.1 Fuzzy matter-element closeness model

The core of the fuzzy matter-element model is to promote the transformation of entities and solve the problem of incompatibility among them. Targeting the incompatibility and fuzziness of various indicators in the system to evaluate the ecological security of tourism, we propose that the problem of incompatibility between indicators can be solved by using fuzzy matter-element analysis [37,38].

3.1.1 Fuzzy matter-element and compound fuzzy matter-element

Assuming that R is a fuzzy matter-element of the ecological security of tourism, U represents indices of the ecological security of tourism, W represents its features, and X represents eigenvalues, then R = (U, W, X) is obtained as follows:

R m n = U 1 U 2 ⋯ U m W 1 X 11 X 21 ⋯ X m 1 W 2 X 12 X 22 ⋯ X m 2 ⋮ ⋮ ⋮ ⋮ W n X 1 n X 2 n ⋯ X m n ,

where R m n is an n-dimensional composite matrix of the fuzzy matter-element of the mth indicator of the ecological security of tourism, U i is the ith index of the ecological security of tourism, i = 1,2,…, m, W j is the jth feature of the ecological security of tourism, j = 1,2,…, n, and X ij is the fuzzy value corresponding to the jth feature of the ith index of the ecological security of tourism.

3.1.2 Standardized optimal degree of subordination

The fuzzy value corresponding to each evaluation index of the ecological security of tourism is called the preferential degree of its membership based on the corresponding degree of the fuzzy value of membership of each corresponding evaluation index in the optimal scheme. Because the preferential degree of membership is generally positive, the following indicators can be used for data standardization:

Bigger and better (+):

β i j = ( X i j − min X i j ) / ( max X i j − min X i j )

Smaller and better (−):

β i j = ( max X i j − X i j ) / ( max X i j − min X i j ) ,

where β i j is the preferential degree of membership and max X i j and min X i j are the maximum and minimum values of X ij corresponding to each feature of each index of the ecological security of tourism, respectively. The standardization of the quantities shows that the preferential degree of membership of the ecological security of tourism is the fuzzy matter-element matrix R ˜ m n , i.e.,

R ˜ m n = U 1 U 2 ⋯ U m W 1 β 11 β 21 ⋯ β m 1 W 2 β 12 β 22 ⋯ β m 2 ⋮ ⋮ ⋮ ⋮ W n β 1 n β 2 n ⋯ β m n .

3.1.3 Difference-squared fuzzy matter-element

In general, the standard fuzzy matter-element R 0 n is determined by the maximum or minimum value of each scheme in the fuzzy matter-element R ˜ m n with optimal membership. If Δ i j (i = 1, 2, …, n; j = 1, 2, …, m) is used to represent the square of the difference between R 0 n and R ˜ m n , this yields the difference-squared composite fuzzy matter-element R Δ of the ecological security of tourism Δ i j = ( β 0 j − β i j ) 2 .

3.1.4 Euclidean closeness

Euclidean closeness is based on the analysis of the fuzzy matter-element model. When Euclidean closeness is used in it, the model can more accurately measure the closeness between the value of each index and the optimal standard and can then rank the advantages and disadvantages of each according to Euclidean closeness. The Euclidean closeness of the ecological security of tourism δ K i is calculated as follows:

δ K i = 1 − ∑ j = 1 n ω j Δ i j ,

where ω j is the comprehensive weight of index j of the ecological security of tourism. The greater the value of δ K i , the closer it is to the optimal standard, i.e., the higher the level of the ecological security of tourism. The Euclidean closeness-based composite fuzzy matter-element R δ K of the ecological security of tourism can then be constructed as follows:

R δ K = U 1 U 2 ⋯ U m δ K i δ K 1 δ K 2 ⋯ δ K m .

3.2 Spatial autocorrelation analysis model

Spatial autocorrelation involves using statistical spatial methods to study the spatial correlation between units and surrounding units. By using GeoDa software to analyze the spatial distribution characteristics of units, global and local indicators are typically used [39,40].

The calculation of global autocorrelation depends on the spatial object in question. Its function is to describe the overall distribution of a given phenomenon, often by using Moran’s global index. When the global spatial correlation is significant, the fully randomized distribution of the local sample may be masked. However, when there is no global statistical index of spatial autocorrelation, the spatial data on the local sample may have a significant correlation with each other. It can thus be used for local spatial analysis, which is commonly expressed by Moran’s local index. The two functions are as follows:

I G = n ∑ i = 1 n ∑ j ≠ 1 n w i j × ∑ i = 1 n ∑ j = 1 n w i j ( x i − x ¯ ) ( x j − x ¯ ) ∑ i = 1 n w i j ( x i − x ¯ ) 2 ,

I L = x i − x ¯ S i 2 ∑ j = 1 , j ≠ i n w i j ( x j − x ¯ ) ,

where n is the total number of spatial units in the study area, x i is the value of the attribute at the ith spatial position, w ij is the spatially adjacent weight matrix, and S i 2 = ∑ j = 1 , j ≠ i n ( x j − x ¯ ) 2 n − 1 − x ¯ 2 .

3.3 Grey relational model

Grey correlation is used to analyze the characteristics of atypical distributions of multiple factors through the geometric relationship of the series of data of the given system. Comparing the degree of correlation between factors in the system can reflect the relationship between “primary and secondary” factors, or the “advantages and disadvantages” of the same reference sequence [11,41]. The grey correlation model is used to identify the main obstacles to the ecological security of tourism in the three northeastern provinces considered here:

O = 1 n − 1 ∑ k = 1 n ξ ( k ) ,

where O is the degree of grey correlation between the subfunction x i (k) and the generating function y(k) of the sequence. The subfunction x i (k) is the standardized value of each evaluation index i of the ecological security of tourism in the three northeastern provinces, and the generating function y(k) is δ K i , ξ ( k ) = 1 1 + Δ x i ( k ) α x i − Δ y ( k ) α y .

3.4 Data sources

The data used for the indices of evaluation of the ecological security of tourism were obtained from the following three sources: (1) the statistical yearbooks of China’s regional economy, statistical yearbooks of its environment (https://www.stats.gov.cn/), statistical yearbooks of China’s cultural relics and tourism (https://data.cnki.net/yearBook/), and statistical yearbooks for the three northeastern provinces and their cities (http://tjj.hlj.gov.cn/;http://tjj.jl.gov.cn/; https://tjj.ln.gov.cn/); (2) statistical reports on cultural and tourism-related development, statistical bulletins on national economic and social development (https://www.stats.gov.cn/tjsj/tjgb/ndtjgb/), and other government reports related to the development of tourism in the three northeastern provinces and their cities in the study period; and (3) official websites of tourism bureaus and statistical bureaus of the three provinces and their cities in Northeast China. Based on the above statistics, for some of the data that exist, the Exponential Smoothing method is used to compensate for missing data in some years by assigning reasonable values.

4 Results and analysis

4.1 Temporal evolution of the ecological security of tourism

4.1.1 Measurement and analysis of the ecological security of tourism of cities (prefectures and regions)

The fuzzy matter-element closeness model was applied to evaluate the ecological security of tourism of the three northeastern provinces from 2010 to 2019. This yielded the Euclidean closeness-based rankings of 34 prefecture-level cities, one state, and one region (Table 2). The values of the Euclidean closeness were used to draw a map of the level of ecological security of tourism of each city, as shown in Figure 1.

Table 2

Results of the evaluation of the ecological security of tourism of cities (prefectures and regions) in Northeast China

City 2010 2013 2016 2019
δ K i Ranking δ K i Ranking δ K i Ranking δ K i Ranking
Shenyang 0.3520 21 0.3588 34 0.3655 34 0.3474 36
Dalian 0.4099 2 0.4005 7 0.3823 26 0.3932 15
Anshan 0.3737 10 0.3788 22 0.3803 28 0.3574 35
Fushun 0.3843 6 0.3925 11 0.3965 14 0.3829 21
Benxi 0.3743 9 0.4039 5 0.3514 36 0.3761 26
Dandong 0.3759 8 0.3934 10 0.3959 17 0.3997 9
Jinzhou 0.3193 35 0.3632 29 0.3860 24 0.3855 19
Yingkou 0.3665 15 0.3788 23 0.3702 33 0.3650 31
Fuxin 0.3393 28 0.3803 20 0.3925 19 0.3909 17
Liaoyang 0.3544 19 0.3688 28 0.3710 32 0.3644 32
Panjin 0.3345 30 0.3597 33 0.3574 35 0.3578 34
Tieling 0.3500 24 0.3898 13 0.3910 20 0.3847 20
Chaoyang 0.3393 27 0.3710 26 0.3787 29 0.3899 18
Huludao 0.3287 32 0.3891 14 0.3757 30 0.3745 29
Changchun 0.3732 11 0.4155 3 0.4303 1 0.4187 3
Jilin 0.3726 13 0.4135 4 0.4102 10 0.4063 7
Siping 0.3310 31 0.3613 30 0.3881 22 0.3762 25
Liaoyuan 0.3171 36 0.3854 17 0.4029 12 0.3935 13
Tonghua 0.3567 18 0.3865 16 0.4015 13 0.3976 10
Baishan 0.3467 25 0.3604 32 0.4055 11 0.4051 8
Songyuan 0.3434 26 0.3797 21 0.3890 21 0.3804 23
Baicheng 0.3202 34 0.3541 35 0.3850 25 0.3757 27
Yanbian Prefecture 0.3902 4 0.4005 6 0.4168 4 0.4263 2
Harbin 0.3667 14 0.3911 12 0.4168 5 0.4110 6
Qiqihar 0.3365 29 0.3483 36 0.3720 31 0.3746 28
Jixi 0.3264 33 0.3611 31 0.3864 23 0.3828 22
Hegang 0.3513 22 0.3888 15 0.4105 9 0.3974 12
Shuangyashan 0.3596 17 0.3837 18 0.3961 16 0.3975 11
Daqing 0.3601 16 0.3937 9 0.4186 3 0.4128 5
Yichun 0.3868 5 0.3957 8 0.4158 6 0.3914 16
Jiamusi 0.3506 23 0.3713 25 0.3822 27 0.3611 33
Qitaihe 0.3776 7 0.4165 2 0.4254 2 0.4370 1
Mudanjiang 0.3926 3 0.3784 24 0.3953 18 0.3677 30
Heihe 0.3727 12 0.3806 19 0.4156 7 0.3771 24
Suihua 0.3533 20 0.3707 27 0.3962 15 0.3933 14
Daxing’anling area 0.4337 1 0.4318 1 0.4136 8 0.4180 4
Mean value 0.3589 0.3833 0.3936 0.3881

Note: Due to limitations of space, only the values of the Euclidean closeness and the corresponding rankings of the cities in 2010, 2013, 2016, and 2019 are listed.

Table 2 and Figure 2 show that the ecological security of tourism of the three northeastern provinces exhibited a trend of fluctuating growth from 2010 to 2019. It increased continuously from 2010 to 2016 and decreased slightly from 2016 to 2019. This reflected steady development and a weakening of fluctuations on the whole. The differences in the ecological security of tourism among cities (prefectures and regions) were not prominent: (1) Liaoyuan, Baishan, Jixi, Fuxin, and Daqing exhibited a significant trend of growth in δ K i from 2010 to 2019 and rose in the overall rankings. Liaoyuan rose from 36th in 2010 to 13th in 2019, indicating that the local government had strengthened protections for the ecological environment while ensuring the stable development of the tourism industry and related industries. The carrying capacity of the environment of tourism has continuously improved in these areas as well. (2) The values of δ K i for Mudanjiang, Anshan, Benxi, Yingkou, and Fushun decreased from 2010 to 2019. Mudanjiang correspondingly dropped in the rankings from third in 2010 to 30th in 2019. Compared to other regions, Mudanjiang’s k-value has not changed much, but its ranking has been decreasing in the context of other cities increasing their investment gradually in tourism and ecological protection. (3) The values of δ K i for Hegang, Jinzhou, Baicheng, and Siping changed significantly from 2010 to 2019. Its values for Hegang and Jinzhou were 0.079 and 0.077, respectively, indicating that there was no significant relationship among the trends of development of these four cities over time. (4) The value of δ K i for Daxing’anling decreased from 2010 to 2019, while that of Qitaihe increased. Minor changes in δ K i had little impact on the rankings. (5) The values of δ K i for other cities fluctuated to varying degrees, with a small increase on the whole.

Figure 2 The levels of ecological security of tourism of cities (prefectures and regions) in Northeast China.
Figure 2

The levels of ecological security of tourism of cities (prefectures and regions) in Northeast China.

Based on the above statistics, for some of the data that exist, the Exponential Smoothing method is used to compensate for missing data in some years by assigning reasonable values. Using ArcGIS to visualize the Euclidean proximity of tourism ecological security in three provinces of Northeast China from 2010 to 2019 (Figure 3), it can be observed that, except for the Daxing’anling area in Heilongjiang Province, the tourism ecological security among the cities (states) in three provinces of Northeast China from 2010 to 2014 spatially presented “low in the west, high in the central and eastern parts.” In 2015, the spatial difference between the cities in the central region was large, and in 2016–2018, the spatial difference was “high in the central part, slightly lower in the western and eastern parts.” By 2019, the overall spatial difference is gradually reduced, showing “slightly higher in the central and eastern parts, slightly lower in the western part.” Over time, the overall presentation of three provinces of Northeast China among the levels of ecological security of tourism of cities (prefectures and regions) has improved significantly, resulting in significant changes in the overall pattern. However, the overall pattern shows that the level of tourism ecological security among cities (states and regions) is still at an increasing stage. This further indicates that tourism ecological security among cities (states and regions) in three provinces of Northeast China is in a stage of continuous development from a low level to a high level from 2010 to 2019, influenced by national and regional policies, the level of tourism development and industrial structure, and differences in ecological environment.

Figure 3 Spatial distribution of the ecological security of tourism in Northeast China.
Figure 3

Spatial distribution of the ecological security of tourism in Northeast China.

4.1.2 Measurement and analysis of the ecological security of tourism among provinces

The Euclidean proximities of the ecological security of tourism of the three provinces were obtained based on the values of their respective cities (Figure 4). Figure 4 shows the following: (1) The value of δ K i for Liaoning Province fluctuated, rising from 0.3572 in 2010 to 0.3764 in 2019, but its ranking dropped from second in 2010 to third in 2019. (2) The value of δ K i for Jilin Province exhibited an “m”-shaped trend of growth, increasing from 0.3501 in 2010 to 0.3978 in 2019, and it rose in the rankings from second in 2010 to first in 2019. (3) The value of δ K i for Heilongjiang Province was stable, and it was constantly ranked in the top two during the study period.

Figure 4 Levels of ecological security of tourism of three provinces in Northeast China.
Figure 4

Levels of ecological security of tourism of three provinces in Northeast China.

The above shows that authorities in Jilin Province have adequately attended to the ecological environment of tourism during its rapid economic development. Heilongjiang Province continues to explore the harmonious development of the tourism economy and its ecological environment on the premise that it has inherent advantages in ecological resources. Liaoning Province has a strong industrial foundation that has a significant impact on the ecological environment of tourism. The ecological security of tourism of all three provinces has gradually developed over the years, but there is considerable room for improvement.

4.2 Analysis of spatial agglomeration of the ecological security of tourism

4.2.1 Analysis of the global spatial pattern

The value of Moran’s I of the ecological security of tourism and related indicators was calculated for the three northeastern provinces by using GeoDa software. The results show that their values of Moran’s I were all positive and passed the test of significance. The results show the following: First, it is unlikely that the ecological security of tourism of the three northeastern provinces was randomly distributed from 2010 to 2019 such that it exhibited spatial agglomeration. That is, there was a positive spatial correlation among the cities of the provinces. Second, the value of Moran’s I gradually decreased from 2010 to 0.037 in 2013, indicating that although the ecological security of tourism had a positive correlation with each province on the whole, the degree of agglomeration gradually decreased over time. After its decline in 2013, Moran’s I gradually rebounded from 2014 to 2019. This is because regions across the country strengthened their policies of pollution control during this period, following the directions of the central government.

4.2.2 Analysis of local spatial patterns

To intuitively analyze the local spatial relationships among cities in the three northeastern provinces, we drew a scatter diagram of the values of Moran’s I (Figure 5) and a local indicator for spatial autocorrelation (LISA) agglomeration map (Figure 6) of the ecological security of tourism from 2010 to 2019 using GeoDa.

Figure 5 Scatter map of values of Moran’s I of the ecological security of tourism in Northeast China.
Figure 5

Scatter map of values of Moran’s I of the ecological security of tourism in Northeast China.

Figure 6 LISA agglomeration map of the ecological security of tourism in Northeast China.
Figure 6

LISA agglomeration map of the ecological security of tourism in Northeast China.

The scatter diagram of Moran’s I yielded the local spatial relationships among the cities. Figure 5 shows the following: (1) The overall pattern of the ecological security of tourism in the three northeastern provinces was stable, and most cities were at similar levels with a certain degree of spatial dependence among them. (2) Most of them were concentrated in the second high & high (HH) and fourth low & low (LL) quadrants, which shows that they had strong local correlations with the ecological security of tourism, especially in 2010. (3) Strong spatial correlations among the cities were observed in 2010, 2016, and 2019, but nearly one-third of them were still located in the first low & high (LH) and third high & low (HL) quadrants from 2010 to 2019. This led to negative correlations and significant spatial differences. In general, the spatial distribution of the ecological security of tourism in all three provinces exhibited agglomeration and dependence among the cities.

The LISA cluster map provides further visual insights into the similarities and differences in both homogeneous and heterogeneous variations of tourism ecological security among cities (states and regions) and their relative geographic distributions. From Figure 6, we can observe the following: (1) Harbin City, located in the central part of the three provinces of Northeast China, maintained a relatively high level of tourism ecological security from 2010 to 2019, except in 2011 (HH type). Cities (states and regions) in the central and eastern parts of Jilin Province and the central and southern parts of Heilongjiang Province, adjacent to Harbin, also exhibited relatively high levels of tourism ecological security. The cities that formed HH clusters gradually expanded outward from Harbin starting in 2010. By 2016, four cities around Harbin were all in the HH cluster, indicating their relatively high levels of tourism ecological security. These cities had fewer differences from their neighboring cities, showing a pattern of localized homogeneity. From 2017 to 2019, cities in the HH cluster remained stable around Harbin in the southern part of Heilongjiang Province, Jilin City in the central and southern parts of Jilin Province, and Baishan City. (2) Jinzhou City, located in the central-western part of Liaoning Province, consistently exhibited a low level of tourism ecological security from 2013 to 2019 (LL type). Similarly, neighboring cities in the central and southern parts of Liaoning Province, including Yingkou, Anshan, and Panjin, also had LL cluster types from 2017 to 2019. This suggests that there was limited spatial differentiation in this region, and significant polarization resulted in a spatial negative correlation. (3) In 2013 and 2016, Daqing City, situated in the central-western part of Heilongjiang Province, had HL-type tourism ecological security, while Dandong City, located along the eastern coast of Liaoning Province, had HL-type tourism ecological security in 2016. This indicates that these cities had relatively high levels of tourism ecological security compared to their neighboring cities, which had relatively low levels (LH type). Cities in HL and LH clusters showed typical negative correlations, indicating strong spatial heterogeneity. In other words, the rise or fall in their own levels of development was not influenced by neighboring cities and did not radiate outward. This analysis highlights the spatial dynamics of tourism ecological security and its clustering patterns among cities (states and regions) in three provinces of Northeast China, shedding light on the complex relationships and regional variations over the years.

4.3 Identifying obstacles to the ecological security of tourism

We used the grey correlation model to calculate the degrees of grey correlation of the 33 factors in the system of indices to assess the ecological security of tourism, and the results are listed in Table 3. We also used the degree of correlation of each index to calculate the degree of grey correlation of each subsystem of indices based on the DPSIR model, and the results are shown in Table 4.

Table 3

Degrees of grey correlation of the factors influencing the ecological security of tourism in cities in three provinces in Northeast China

City Project Ranking City Project Ranking
1 2 3 4 5 1 2 3 4 5
Cities (prefectures and regions) in three northeastern provinces
Panjin Index P₃ S₃ D₃ I₄ S₅ Tonghua Index S₃ R₇ P₃ S₇ P₇
Correlation degree 0.987 0.983 0.982 0.977 0.974 Correlation degree 0.983 0.980 0.969 0.966 0.958
Mudanjiang Index P₄ D₃ P₁ R₂ P₃ Dandong Index P₃ R₂ D₃ S₇ S₃
Correlation degree 0.983 0.982 0.981 0.980 0.978 Correlation degree 0.981 0.978 0.975 0.975 0.971
Heihe Index D₃ P₅ P₇ P₃ P₁ Jiamusi Index P₇ P₆ S₅ S₃ P₃
Correlation degree 0.980 0.978 0.975 0.975 0.974 Correlation degree 0.979 0.977 0.975 0.969 0.969
Yichun Index P₃ S₇ P₆ P₇ P₁ Daqing Index D₃ P₆ S₃ R₃ P₇
Correlation degree 0.978 0.976 0.975 0.973 0.972 Correlation degree 0.977 0.976 0.967 0.965 0.964
Three provinces in Northeast China
Liaoning Index I₅ P₇ R₆ R₂ D₁ Heilongjiang Index P₇ P₆ I₅ I₄ P₁
Correlation degree 0.992 0.991 0.991 0.990 0.987 Correlation degree 0.985 0.983 0.978 0.977 0.973
Jilin Index D₁ P₆ I₂ I₃ R₅
Correlation degree 0.963 0.947 0.946 0.939 0.938

Note: Due to limitations of space, we list only the eight cities that have encountered obstacles with degrees of correlation higher than 0.975. See Table 1 for all obstacles.

Table 4

Degrees of grey correlation of obstacles influencing the ecological security of tourism in Northeast China based on the DPSIR model

D Correlation degree Ranking P Correlation degree Ranking S Correlation degree Ranking
D₁ 0.9888 6 P₁ 0.9813 14 S₁ 0.9351 31
D₂ 0.9887 7 P₂ 0.9820 12 S₂ 0.9597 23
D₃ 0.9805 15 P₃ 0.9765 17 S₃ 0.9443 28
D₄ 0.9368 30 P₄ 0.9819 13 S₄ 0.9280 32
D₅ 0.9541 26 P₅ 0.9864 8 S₅ 0.9833 10
D₆ 0.9708 21 P₆ 0.9914 3 S₆ 0.9592 24
D₇ 0.9680 22 P₇ 0.9936 1 S₇ 0.9579 25
I Correlation degree Ranking R Correlation degree Ranking
I₁ 0.9251 33 R₁ 0.9744 19
I₂ 0.9485 27 R₂ 0.9833 9
I₃ 0.9825 11 R₃ 0.9762 18
I₄ 0.9911 4 R₄ 0.9774 16
I₅ 0.9926 2 R₅ 0.9742 20
R₆ 0.9908 5
R₇ 0.9438 29

Note: See Table 1 for obstacles at all levels.

4.3.1 Identifying obstacles in cities and provinces

Because many factors were considered in the system of indices of the ecological security of tourism of the three northeastern provinces, we chose the top five in terms of the degree of grey correlation to identify the main obstacles to further ecological development. Table 3 shows the following: (1) There were significant differences in obstacles to the ecological security of tourism among the cities and the provinces, and many of them had a degree of grey correlation higher than 0.975. (2) The urban population density P3 is now the main obstacle affecting the ecological security of tourism in Panjin, Dandong, and Yichun. The number of A-class tourist spots in D3 has become the main obstacle factor affecting the ecological security of tourism in Heihe and Daqing. The cultivated area of land per capita S3, per capita daily domestic water consumption P4, and industrial sulfur dioxide emissions P7 are the main obstacles affecting Tonghua and Mudanjiang. With an increase in the urban population, the declining popularity of scenic spots as well as agriculture, industry, area of land, and water influence the ecological environment of tourism. (3) Industrial sulfur dioxide emissions P7 are the main obstacle to the ecological security of tourism in all three provinces in Northeast China, with a degree of correlation of 0.992. In addition, the main obstacles to tourism include employment in tourism R6, the rate of growth in income from it D1, and the density of tourists P1.

4.3.2 Identifying obstacles to each subsystem

We used the obstacles identified to the development of the ecological security of tourism in each city to calculate its degree of grey correlation based on the factors influencing each subsystem of the DPSIR model from 2010 to 2019. Table 4 shows the following: (1) Around 25% of indicators – the top eight – in the system were major factors influencing the ecological security of tourism in the three northeastern provinces. From high to low, these factors were industrial sulfur dioxide emissions (P7), per capita rate of growth in disposable income (I5), industrial wastewater emission (P6), per capita rate of growth in consumption-related expenditure (I4), number of employees in tourism (R6), rate of growth of income from tourism (D1), rate of growth in the number of tourists (D2), and smoke and dust emissions (P5). (2) The degrees of grey correlation of the subsystems of the DPSIR model were of the order “P > R > D > S > I,” indicating that the pressure system is now the main obstacle affecting the ecological security of tourism in the three provinces.

5 Discussion

The ecological security of tourism in the provinces of Northeast China considered here showed a steady development from 2010 to 2019, but this does not guarantee that this trend can be maintained. The above shows that there are many obstacles to the ecological security of tourism in the three northeastern provinces and are mainly related to the industrial and tourism economies (this conclusion is consistent with Zhao and Kong [42]). As the economic development of the three provinces considered here mainly relies on heavy industry, coal consumption during the heating period in winter increases significantly, and the resulting increase in emissions affects the ecological environment. The unbalanced development of tourism, significant regional differences in tourism-related resources, and the uneven quality of tourism facilities also hinder improvements in the ecological security of tourism (this conclusion is consistent with Xu et al. [22]).

In general, the ecological security of tourism δ K i of the three northeastern provinces has improved continuously, the differences among cities have shrunk, and the spatial distribution pattern has significant differences. This is related to the tourism-based economic development of cities, the quality of the social life of residents, the importance of the ecological environment, and the state of tourism facilities. While maintaining the trend of rapid economic development, relevant authorities should strengthen the protection of the tourism ecological environment. This can be achieved by encouraging the use of green energy, establishing a comprehensive tourism infrastructure, harmonizing the relationship between tourists and the natural environment, and implementing differentiated tourism development strategies. These measures will contribute to the gradual improvement of the ecological security level of the tourism industry in the three northeastern provinces considered in this study. The tourism ecosystem is a complex system composed of natural, economic, social, and environmental factors. The rapid development of the regional economy, accelerating urbanization, and damage to the ecological environment seriously threaten the local ecological security of tourism (this conclusion is consistent with Wang et al. [14] and He et al. [43]).

Here, we analyzed only the spatiotemporal patterns of the ecological security of tourism and the obstacles to it. Further research is needed on the dynamic mechanism of the ecological security of tourism. Moreover, we focused here on the municipal level owing to the rich and diverse types of resources for tourism in the three northeastern provinces considered in this study. Future research should consider multidisciplinary and multi-perspective examinations of the ecological security of tourism in these northeastern provinces. At the same time, this study was conducted only on a single spatial scale (city level), and it is one of the crucial issues to be discussed for future research on how to further evaluate tourism ecological security using a multidisciplinary intersection and multi-scale coupling. Finally, because the ecological security of tourism undergoes dynamic changes, it is necessary to establish a long-term mechanism to monitor it to provide an accurate basis for systematic research in the area.

6 Conclusion

In this study, we used the DPSIR model to construct a system of indices to assess the ecological security of tourism in three northeastern provinces of China. We introduced the fuzzy matter-element model of closeness to it and used models of spatial autocorrelation and grey correlation to analyze the trend of development of the ecological security of tourism from 2010 to 2019.

  1. First, fluctuations in the absolute values of the ecological security of tourism δ K i of cities in the study area did not have prominent spatiotemporal characteristics. The average value changed from 0.3589 in 2010 to 0.3881 in 2019. The overall trend is upward volatility and development. However, the average value of δ K i is still far from its optimal value, indicating that there is considerable room for improving the ecological security of tourism in the three northeastern provinces. Second, spatial patterns of the ecological security of tourism in the three northeastern provinces have continuously developed from “high in the west and low in the middle and east” in 2010 to “slightly higher in the middle-east and slightly lower in the west” in 2019. Third, the ecological security of tourism δ K i of the three provinces has steadily improved. The rankings of their cities in 2019 have changed significantly compared with those in 2010. Jilin Province moved from third in 2010 to first in 2019. Fourth, the ecological security of tourism exhibits prominent characteristics of spatial agglomeration. The imbalance in tourism development among cities and differences in measures to protect the ecological environment have led to a decline in this agglomeration over time. Fifth, most economically developed cities in the middle regions of the three provinces exhibited strong local autocorrelation, and a small number of cities in the west had a prominent negative correlation, which reflects spatial heterogeneity.

  2. To account for obstacles to ecological protection, we first identified the main obstacles to this in cities and provinces from 2010 to 2019. We found that urban population density, the number of class A tourist attractions, per capita area of cultivated land, per capita daily domestic water consumption, and industrial sulfur dioxide emissions are the main obstacles to the ecological security of tourism. Industrial sulfur dioxide emissions become the highest impediment to tourism ecological safety in each of the three northeastern provinces. Second, the DPSIR model showed that the pressure system is the main factor hindering improvements in the ecological security of tourism in the three northeastern provinces.

In this study, the DPSIR model is combined with social, economic, and environmental factors to assess tourism ecological security in three provinces of Northeast China. The research findings can provide decision support for regional tourism ecological security system management and the formulation of sustainable tourism policies in three provinces of Northeast China. In addition, they can serve as a theoretical reference for the development of similar regions.

  1. Funding information: It is a part of the Natural Science Foundation of China project (41801165).

  2. Author contributions: J.L. and D.S. designed the research; J.G. and D.W. performed the research and analyzed the data; D.S. and J.G. wrote the article. D.S. and J.G. are co-first authors of the article.

  3. Conflict of interest: No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication.

  4. Data availability statement: Data will be made available on request.

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Received: 2023-03-22
Revised: 2023-09-06
Accepted: 2023-09-07
Published Online: 2024-04-09

© 2024 the author(s), published by De Gruyter

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

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  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0545
Keywords for this article
tourism ecological security; dynamic spatiotemporal evolution; DPSIR model; fuzzy matter-element model; Northeast China
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  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
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  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
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  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
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  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
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  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
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  33. Assessing the ecological security of tourism in Northeast China
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  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
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  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
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  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
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  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
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  49. New database for the estimation of dynamic coefficient of friction of snow
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  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
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  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
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  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
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  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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