Research Articles

Performance evaluation of resource-based city transformation: A case study of energy-enriched areas in Shaanxi, Gansu, and Ningxia

  • WEN Qi , 1 ,
  • FANG Jie 2 ,
  • SHI Linna 2 ,
  • WU Xinyan 2 ,
  • LUO Anmeng 2 ,
  • DING Jinmei , 2, *
  • 1. School of Architecture, Ningxia University, Yinchuan 750021, China
  • 2. School of Geography and Planning, Ningxia University, Yinchuan 750021, China
* Ding Jinmei (1982-), Associate Professor, specialized in specialized in resource and ecological security research. E-mail:

Wen Qi (1979-), Professor, specialized in rural geography and energy economic research. E-mail:

Received date: 2022-12-06

  Accepted date: 2023-07-20

  Online published: 2023-11-15

Supported by

National Natural Science Foundation of China(42271221)

National Natural Science Foundation of China(42061037)


Resource-based cities are important bases for resources and energy in China. However, the world and the country’s sustainable development goals require them to undergo transformation. The complexity of this transformation poses challenges for these cities. This study aims to evaluate the transformation performance of resource-based cities in Shaanxi, Gansu, and Ningxia. The findings will help understand their capabilities and achievements in transformation and provide guidance for future transformation planning. To evaluate the transformation performance, this study employs the entropy weight Technique for Order of Preference by Similarity to Ideal Solution method. An index system is constructed, including the industrial diversification and specialization indices. These indices serve as benchmarks for assessing the transformation performance. The period 2010-2019 is considered, and the transformation performance of resource-based cities is evaluated based on different development stages and regions. The results reveal the following insights: (1) Most resource-based cities demonstrate favorable transformation performance. Although variations exist between cities, the gaps are gradually narrowing. (2) Over an extended period, the transformation performance of each city undergoes continuous changes, with high-performing areas shifting. (3) The transformation performance of resource-based cities varies significantly across different development stages. (4) An imbalance exists among the regions where resource-based cities are located, and a diffusion effect can be observed. Accordingly, the following enlightenment and policy suggestions are obtained: (1) exploring targeted management policies for resource-based cities; (2) fostering a dynamic and open transformation environment; (3) promoting the concept of regional cooperation in transformation; (4) improving the business environment; (5) promoting enterprise innovation; (6) establishing and improving a long-term mechanism for sustainable development and a compensation mechanism for resource development; and (7) optimizing the talent training system.

Cite this article

WEN Qi , FANG Jie , SHI Linna , WU Xinyan , LUO Anmeng , DING Jinmei . Performance evaluation of resource-based city transformation: A case study of energy-enriched areas in Shaanxi, Gansu, and Ningxia[J]. Journal of Geographical Sciences, 2023 , 33(11) : 2321 -2337 . DOI: 10.1007/s11442-023-2178-7

1 Introduction

A resource-based city is constructed or developed by relying on the natural resources available in the region, with resource development through extraction and processing as its leading industry. Since 1949, resource-based cities in China have made significant contributions to energy and resource supply, the establishment of the national industrial system, and the rapid development of the national economy during the reform and opening up period (The State Council of the People’s Republic of China, 2013). However, some of these resource-based cities, particularly those in mining regions, have become overly reliant on the resource-based industries and have neglected long-term planning for ecological and environmental needs as well as economic development. Consequently, the development of resource-based cities has resulted in numerous economic, social, and environmental problems (He et al., 2017; Wen et al., 2022). These include the depletion of resources, a deteriorating ecological environment, limited development of follow-up industries, and an increasing unemployment rate. The report of the 20th National Congress of the Communist Party of China mentioned the “speed up the green transformation of development mode” and “actively yet prudently promote carbon peak carbon neutrality,” (Xi, 2022, p.1) and the Fourth Plenary Session of the 19th CPC Central Committee used the phrase “adjusted structure, promoted reform, protected ecology, and benefited people’s livelihood” to indicate the imminent transformation of resource-based cities (The Fourth Plenary Session of the Nineteenth Central Committee of the Communist Party of China, 2019). However, transforming resource-based cities is a complex and systematic project. Among the 262 resource-based cities in China, most are still in the process of transformation and face various difficulties such as insufficient momentum for transformation and an unclear path for transformation (Li et al., 2013; Wang et al., 2022). Therefore, it is crucial to examine the problems associated with the transformation of resource-based cities in order to find ways to alleviate these predicaments.
Transformation refers to the fundamental process of changing structural forms, operating models, or people’s concepts. Existing research on transformation can mostly be divided into three categories: industrial level transformation research, urban level transformation research and regional level transformation research. These are the transformation studies from the perspective of a certain industry, a certain city and a certain urban agglomeration or region. At the industrial level, that is, the transformation research of an industry or multiple industries, scholars have analyzed the relationship between industrial transformation and the status of certain factors, such as innovation capability and regulatory planning (Long et al., 2013). These studies focus on the impact strength of each factor and its relationship with industrial transformation (Yang et al., 2019; Zhang et al., 2020; Aleksanda, 2023; Sancak, 2023). At the urban level, it is a comprehensive analysis of the various elements of a city’s transformation, there are numerous studies on the characteristics and influencing factors of urban transformation and development (Yan et al., 2019; Wang et al., 2021a; Rose et al., 2022). At the multi-city or regional level, the existing studies mostly provide overall research of regional resource-based cities, such as the Yellow River basin and regions. Most of these studies comprehensively analyze each city’s conditions by dividing and comparing resource-based cities at different development stages in the region (Hu and Yang, 2019; Chen et al., 2020; Fu et al., 2020; Li et al., 2020; Wang et al., 2021b; Wang et al., 2023).
In the literature, performance evaluation refers to the use of certain methods, quantitative indicators, and evaluation standards to conduct regular or irregular assessments of the achievements and effects of the evaluation object. Research in this area can be classified in terms of the performance evaluation of the research object and its determinants, the temporal and spatial differentiation of the research object and its influencing factors, the effect of the performance evaluation on the research object, and the innovativeness of the performance evaluation field or method.
Many studies focus on the performance evaluation and influencing factors of a certain object. The research scale ranges from countries and regions to individual cities (Tan et al., 2016; Cui et al., 2021; O’Keeffe et al., 2022; Wen et al., 2023). And the research objects range from social entities to policy institutions, such as resource markets, operational performance (Chen et al., 2021; Weldesilassie and Worku, 2022; Ye et al., 2023), ecological environment performance (Chng et al., 2022; Dou et al., 2023). Moreover, some scholars have also analyzed the temporal and spatial differentiation of performance, such as the temporal and spatial evolution of urban carbon emission performance (Li and Dewan, 2017; Lv et al., 2021) and regional tourism development patterns (Li et al., 2022). In addition, performance evaluation can affect the research object, such as in terms of its impact on risk taking and the pressure of the evaluators (Frimanson et al., 2021; Do et al., 2022). Furthermore, emerging fields are rich in innovative performance evaluation methods, such as the performance evaluation of enterprise green supply chain,the Internet of Things and low carbon city pilot effect evaluation (Sun et al., 2017; Jin, 2021; Chetlapalli et al., 2022; Zeng et al., 2023; Zhao et al., 2023). Moreover, new methods of performance evaluation methods are constantly evolving (Fan and Zhang, 2021; Liu et al., 2021a; Zhong et al., 2021; Zhou et al., 2021).
Currently, urban transformation studies primarily focus on the overall transformation of a country or the evaluation of resource-depleted areas with the aim of revitalization. However, these studies have often overlooked the variations in characteristics among resource-based cities at different stages of development. Resource-based cities in the growth, maturity, and regeneration stages also require active transformation. Shaanxi, Gansu and Ningxia are such representative regions (Yu et al., 2019). Accordingly, this study examines Shaanxi, Gansu, and Ningxia, which are mainly categorized as growing and mature cities and maintain high resource output. On the basis of analyzing the transformation performance of each city, it is evaluated according to different development stages and different provinces, and their temporal and spatial differences are also analyzed.

2 Research methods and data sources

2.1 Performance measurement of resource-based city transformation

The entropy weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is an improvement over the traditional TOPSIS evaluation method. In this method, the weight of the evaluation index and ranking of the evaluation object are determined by the entropy weight and TOPSIS methods. The entropy weight method is used to objectively determine the weight according to the information provided by each evaluation index. This reflects not only the importance of an index in the index system during decision-making but also the change in index weight. The core idea of the TOPSIS method is to define the distance between the optimal solution and the worst solution of the decision-making problem, and finally calculate the relative closeness of each solution to the ideal solution to rank the pros and cons of the solutions. A combination of the TOPSIS and information entropy methods can effectively eliminate the influence of subjective factors. The main calculation steps are as follows:
① Assuming that there are m objects to be evaluated and that each evaluation object has n evaluation indicators, a judgment matrix is constructed:
$X={{\left( {{X}_{ij}} \right)}_{m\text{*}n}}\left( i=1,2,\ldots,m;j=1,2,\ldots,n \right)$
② The judgment matrix is then standardized as follows:
③ Information entropy is calculated as:
${{H}_{j}}=-k\underset{i=1}{\overset{m}{\mathop \sum }}\,{{P}_{ij}}\ln {{P}_{ij}}$
In the equation,${{P}_{ij}}=\frac{{{{{x}'}}_{ij}}}{\mathop{\sum }_{i=1}^{m}{{{{x}'}}_{ij}}};k=\frac{1}{\ln m}$.
④ The weight of index j is then defined: ${{\omega }_{j}}=\frac{1-{{H}_{j}}}{\mathop{\sum }_{j=1}^{n}\left( 1-{{H}_{j}} \right)}$
where ${{\omega }_{j}}\in \left[ 0,1 \right]$, and $\underset{j=1}{\overset{n}{\mathop \sum }}\,{{\omega }_{j}}=1$.
⑤ The weighting matrix is calculated as follows:
$R={{\left( {{r}_{ij}} \right)}_{m\text{*}n}},{{r}_{ij}}={{\omega }_{j}}\cdot {{{x}'}_{ij}}\left( i=1,2,\ldots,m;j=1,2,\ldots,n \right)$
⑥ The optimal solution $S_{j}^{+}$and the worst solution $S_{j}^{-}$ are determined accordingly:
$S_{j}^{+}=\text{max}\left( {{r}_{1j}},{{r}_{2j}},\ldots,{{r}_{nj}} \right),S_{j}^{-}=\min \left( {{r}_{1j}},{{r}_{2j}},\ldots,{{r}_{nj}} \right)$
⑦ The Euclidean distance between each scheme and the optimal and worst solutions are as follows:
$sep_{i}^{+}=\sqrt{\underset{j=1}{\overset{n}{\mathop \sum }}\,{{\left( S_{j}^{+}-{{r}_{ij}} \right)}^{2}}},sep_{i}^{-}=\sqrt{\underset{j=1}{\overset{n}{\mathop \sum }}\,{{\left( S_{j}^{-}-{{r}_{ij}} \right)}^{2}}}$
⑧ Finally, the comprehensive evaluation index is calculated:
${{C}_{i}}=\frac{sep_{i}^{-}}{sep_{i}^{+}+sep_{i}^{-}},{{C}_{i}}\in \left[ 0,1 \right]$
where large Ci value indicates better evaluation object.

2.2 Construction of an indicator system

In the study of the energy industry, Azapagic and Yu developed a sustainability indicator framework consisting of economic, environmental, social and comprehensive indicators (Pettijohn et al., 2001). Sustainability indicators developed by the Global Reporting Initiative (GRI) have also become the most widely accepted way to assess sustainable development (Michael et al., 2014). Excellent industrial structure is the key to urban transformation. The proportion of tertiary industry reflects the optimization level of regional industrial structure. Diversified industrial layout helps to provide choices for the realization of new development paths. Therefore, on the basis of the above sustainable indicators, this paper introduces the index of industrial specialization and industrial diversification. According to the National Plan for Sustainable Development of Resource-Based Cities (2013-2020), an indicator system for measuring the transformation performance of resource-based cities is established (Table 1).
Table 1 Resource-based city transformation performance measurement indicators
System layer Orientation layer Measure layer
Regional gross domestic product (GDP) (108 yuan)
GDP per capita (yuan)
Economic structure Value added by industry as a proportion of the GDP (%)
Value added by the tertiary industry as a proportion of the regional
GDP (%)
Industrial transformation Industrial specialization index
Industrial diversification index
Improvements in people’s livelihood Standard of living Per capita disposable income of urban residents (yuan)
Per capita disposable income of rural residents (yuan)
Basic guarantee Number of hospital beds (units)
Number of regular colleges and universities (number)
The growth rate of employees in the secondary and tertiary industries (%)
Resource conservation Energy
Industrial smoke (powder) dust emissions (tons)
Industrial smoke (powder) dust emissions per unit of GDP (ton/108 yuan)
Industrial wastewater discharge (10,000 tons)
Discharge of industrial wastewater per unit of GDP (ton/108 yuan)
Environmentally friendly Green
Green coverage rate of built-up area (%)
Sewage treatment rate (%)
Harmless treatment rate of domestic waste (%)
Comprehensive utilization rate of solid waste (%)

Note: The specialization index and diversification index are calculated based on the number of employed persons in urban units by industry in each city, and are calculated by the Krugman index and the entropy index, respectively.

2.2.1 Industrial specialization index

${{K}_{i}}=\underset{j=1}{\overset{m}{\mathop \sum }}\,\left| {{V}_{ij}}-{{V}_{nj}} \right|,$
where Ki is the Krugman index; Vij represents the proportion of the number of employed persons in the jth industry in city i to the total number of employed persons in the region; Vnj represents the proportion of the number of employed persons in the jth industry in the country to the total number of employed persons in the unit; and m is the number of industries. Higher the value of Ki, higher the degree of industrial specialization in the city.

2.2.2 Industrial diversification index

${{E}_{i}}=\underset{j=1}{\overset{m}{\mathop \sum }}\,\left[ {{S}_{ij}}\cdot \ln \left( 1/{{S}_{ij}} \right) \right],$
where Ei is the entropy index; and Sij represents the proportion of the number of employed persons in the jth industry in city i to the total number of employed persons in the region; The higher the value of Ei, the higher the level of regional diversification.

2.3 Data sources

The study sample consists of data from 14 resource-based cities located in Shaanxi province, Gansu province, and Ningxia Hui autonomous region. These cities are categorized into four groups: growing, mature, declining, and regeneration, based on the National Sustainable Development Plan for Resource-Based Cities (2013-2020). Among them, there are eight growing cities (Yan’an, Xianyang, Baoji, Yulin, Jinchang, Wuwei, Qingyang, Longnan), two mature cities (Weinan, Pingliang), three declining cities (Tongchuan, Baiyin, Shizuishan), and one regenerating city (Zhangye). Shaanxi has six resource-based cities (Yan’an, Tongchuan, Weinan, Xianyang, Baoji, Yulin), Gansu has seven (Jinchang, Baiyin, Wuwei, Zhangye, Qingyang, Pingliang, Longnan), and Ningxia has one (Shizuishan).
This study analyzes the panel data from 2010 to 2019 to comprehensively evaluate the transformation performance of resource-based cities. This period covers the situation before policy guidance and when the policy is implemented. In addition, years of 2010, 2013, 2016, and 2019 are selected to analyze the temporal and spatial evolution of the transformation performance of these cities. The data used in this study are from the 2010-2019 China Urban Statistical Yearbook, Shaanxi Statistical Yearbook, Gansu Development Yearbook, and Ningxia Statistical Yearbook. By conducting this analysis, the study aims to provide a comprehensive assessment of the transformation processes in resource-based cities, shedding light on their performance over time and across different regions.

3 Analysis of results

3.1 Overall patterns of resource-based city transformation performance

Table 2 suggests that while resource-based cities in the studied regions have made progress overall in their transformation efforts, there are still significant disparities among individual cities.
Table 2 Comprehensive scores of the transformation performance of resource-based cities in Shaanxi, Gansu, and Ningxia: 2000-2019
City 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Yan’an 0.317 0.306 0.287 0.310 0.275 0.280 0.302 0.256 0.242 0.259
Tongchuan 0.232 0.229 0.217 0.261 0.193 0.203 0.246 0.214 0.211 0.238
Weinan 0.261 0.270 0.263 0.277 0.258 0.289 0.318 0.372 0.278 0.298
Xianyang 0.771 0.705 0.696 0.725 0.743 0.680 0.727 0.619 0.549 0.593
Baoji 0.356 0.348 0.310 0.322 0.291 0.314 0.382 0.378 0.316 0.334
Yulin 0.440 0.394 0.383 0.379 0.407 0.369 0.408 0.309 0.367 0.438
Jinchang 0.248 0.228 0.234 0.224 0.205 0.234 0.271 0.283 0.319 0.234
Baiyin 0.169 0.270 0.188 0.238 0.199 0.248 0.252 0.219 0.223 0.235
Wuwei 0.221 0.215 0.243 0.251 0.295 0.339 0.345 0.302 0.261 0.313
Zhangye 0.214 0.287 0.237 0.279 0.237 0.314 0.306 0.352 0.289 0.321
Qingyang 0.214 0.227 0.257 0.228 0.192 0.255 0.264 0.256 0.245 0.280
Pingliang 0.186 0.170 0.181 0.198 0.219 0.293 0.307 0.269 0.276 0.292
Longnan 0.214 0.216 0.288 0.243 0.236 0.330 0.280 0.251 0.390 0.374
Shizuishan 0.236 0.235 0.230 0.252 0.197 0.209 0.243 0.206 0.199 0.242
The overall transformation performance of the 14 resource-based cities in Shaanxi, Gansu, and Ningxia exhibited a positive upward trend. However, there were significant variations in the transformation performance among these cities. Xianyang and Yulin stood out with notably favorable transformation performances (refer to Table 2). This can be attributed to their advantageous geographical locations, which enhance accessibility, and their solid foundations in terms of economic and social development. Moreover, the comprehensive scores of cities within the same year exhibited substantial disparities. For instance, in 2019, the comprehensive score of Xianyang city was 2.53 times higher than that of Jinchang city in the same year. Additionally, the comprehensive score of a city showed variations across different years. From 2010 to 2019, Longnan experienced a remarkable 74.77% increase in its comprehensive score, while Shizuishan’s score only increased by 2.54%. In 2019, the comprehensive scores of Yan’an, Xianyang, Yulin, and Jinchang were lower than their respective scores in 2010, with Xianyang experiencing a significant decline of 23.09%, whereas Yulin’s decline was only 0.45%. The magnitude of annual differences in 2019 decreased significantly compared to that of 2010.In 2019, Xianyang city, which had the highest comprehensive score, surpassed Jinchang city, which had the lowest score, by 0.359 points. In contrast, in 2010, Xianyang city’s comprehensive score exceeded that of Baiyin city, the lowest-scoring city, by 0.602 points. Most cities’ comprehensive scores in 2019 were higher than their scores in 2010, indicating an overall improvement in their situations. Table 2 also indicates that the substantial differences in transformation performance among resource-based cities across different regions and years have been steadily narrowing. Efforts should be directed towards addressing the specific challenges faced by each city, ensuring sustainable and balanced development across the region.

3.2 Spatiotemporal characteristics of transformation performance

The resource-based cities of Shaanxi, Gansu, and Ningxia are further divided into weakest, weak, medium, strong, and strongest transformation performance cities using natural discontinuity points.
Using the ArcGIS 10.6 software, the cross-sectional data in 2010, 2013, 2016 and 2019 are selected to map the temporal and spatial evolution of the transformation performance levels of resource-based cities in Shaanxi, Gansu, and Ningxia (as shown in Figure 1), and their performance evaluation results and changes are further analyzed.
Figure 1 Distribution of comprehensive transformation performance in Shaanxi, Gansu and Ningxia in 2010, 2013, 2016 and 2019

3.2.1 Temporal volatility and spatial transfer of resource-based cities’ transformation performance

In terms of time and space evolution, the cities of Yulin and Xianyang have consistently shown strong transformation performance. In 2010, there were several cities with the weakest transformation performance, namely Tongchuan, Zhangye, Wuwei, Baiyin, Pingliang, Qingyang, Longnan, and Shizuishan. Additionally, Weinan and Jinchang were categorized as cities with weak transformation performance. On the other hand, Baoji demonstrated a city with strong transformation performance. Yulin and Xianyang stood out as the only cities with the strongest transformation performance. Compared to 2010, there was a significant decrease in the number of cities with the weakest transformation performance in 2019. Only four cities, namely Tongchuan, Jinchang, Baiyin, and Shizuishan, remained in this category. However, Yan’an and Qingyang continued to exhibit a weak transformation performance. In 2019, the number of cities with medium transformation performance increased, and it included Baoji, Weinan, Pingliang, Zhangye, and Wuwei. Two cities, Yulin and Longnan, were identified as having strong transformation performance, while Xianyang remained the sole city with the strongest transformation performance.
In terms of space, Xianyang and Yulin have consistently maintained strong or even the strongest transformation performance scores. This can be attributed to Shaanxi’s role as a gateway for east-west industrial transfer activities. Xianyang and Yulin have emerged as key players in the province, with Xianyang ranking second and Yulin ranking third in terms of gross products. Additionally, Yulin is known for its abundant energy and mineral resources, making it an important center in the country.In terms of time, there has been a steady improvement in the transformation performance scores of previously weak-performing cities such as Weinan, Baiyin, Wuwei, Zhangye, Qingyang, Pingliang, Longnan, and Shizuishan. The ongoing urban transformation efforts have contributed to the progress in these cities. However, it is worth noting that some cities, like Baoji, have experienced a decline in their transformation performance scores, indicating potential challenges or setbacks in their development.

3.2.2 Differences in transformation performance of resource-based cities at different development stages

Table 3 reveals that the comprehensive scores for urban transformation performance have increased in 2019 compared to their 2010 levels, across various stages.
Table 3 Transformation performance of resource-based cities at various stages of development
Growing Mature Declining Regeneration
Yan’an, Xianyang, Baoji, Yulin, Jinchang, Wuwei, Qingyang, Longnan Weinan, Pingliang Tongchuan, Baiyin, Shizuishan Zhangye
2010 0.348 0.295 0.212 0.214
2011 0.330 0.277 0.245 0.287
2012 0.337 0.321 0.212 0.237
2013 0.335 0.313 0.250 0.279
2014 0.331 0.291 0.196 0.237
2015 0.350 0.239 0.220 0.314
2016 0.372 0.238 0.247 0.306
2017 0.332 0.222 0.213 0.352
2018 0.336 0.220 0.211 0.289
2019 0.353 0.224 0.238 0.321
However, it is notable that the transformation performance of mature cities has deteriorated, with the current transformation effects being less evident. On the other hand, growth-oriented cities have persistently had the highest comprehensive transformation performance scores. Regenerative cities have also experienced rapid growth in their transformation performance. In 2019, their comprehensive scores ranked second, just below that of growth-oriented cities and significantly higher than those of mature and declining cities. This suggests that regenerative cities have effectively revitalized and rejuvenated their urban systems and have become promising centers of development. Renewable resource-based cities have gradually established themselves as emerging leaders in industries associated with renewable resources. Growing and mature cities still maintain certain resource advantages, allowing them to gradually reduce their dependence on leading industries while maintaining a stable economic level. This stability provides a solid foundation for the cultivation and development of emerging industries. In contrast, declining cities encounter challenges in maintaining a high level of economic operation and the exclusive dependence on the energy industry. These difficulties hinder their transformation and pose obstacles to their overall development.

3.2.3 Resource-based cities are still unbalanced among the transition regions, and the diffusion effect appears

Cities with higher transformation performance are significantly concentrated in Shaanxi, indicating that regional imbalances still exist (Table 4). However, the transformation performance score of Shaanxi in 2019 is lower by 0.036 compared to that in 2010. Compared with Gansu and Ningxia, Shaanxi is the material and energy exchange hub between the east and the west in China. It has a long history of development and a solid foundation for development. Based on excellent conditions, it maintains a high transformation performance all year round. With the deepening of urban transformation and the emergence of results, its performance started to gradually decrease. In contrast, Gansu experienced continuous growth, with a relatively large growth rate. Although Gansu had the weakest transformation performance among the three in 2010, it witnessed an impressive increase of 39.77% to reach a score of 0.293 in 2019, indicating significant progress in its urban transformation. Shizuishan, the sole resource-based city in Ningxia, exhibited slow but sustained growth in its transformation performance.
Table 4 Transformation performance of resource-based cities in the three provincial-level regions
Year Shaanxi Gansu Ningxia
2010 0.396 0.209 0.236
2011 0.375 0.230 0.235
2012 0.359 0.233 0.230
2013 0.379 0.237 0.252
2014 0.361 0.226 0.197
2015 0.356 0.287 0.209
2016 0.397 0.289 0.243
2017 0.358 0.276 0.206
2018 0.327 0.286 0.199
2019 0.360 0.293 0.242

4 Discussion and conclusions

4.1 Discussion

4.1.1 Relationship between the transformation of resource-based cities and the level of regional socioeconomic development and geographical conditions

Our findings reveal a notable divergence in the performance of weak and high-performance cities. Weak-performance cities have demonstrated consistent improvement, while high-performance cities have experienced a slight decline. This study posits that cities like Xianyang and Yulin, which exhibit high transformation performance, benefit from their closer proximity to the eastern region and the resulting enhanced accessibility to various resources and opportunities. However, it is important to consider that high-performance cities may encounter challenges due to the scale of their existing industries, which can bring about substantial transformation risks and resistance. Therefore, this paper suggests that the transformation of resource-based cities is closely intertwined with the level of regional economic and social development, as well as the geographical environmental conditions (Sun et al., 2015). These factors significantly impact the complexity, speed, and feasibility of industrial restructuring efforts. This is consistent with Chen et al. (2018) and Niu (2021).

4.1.2 Higher growth rate of transformation performance of growth-oriented cities compared to other types of cities

In cities with a growth-oriented approach, the industrial structure is characterized by flexibility, enabling them to readily embrace emerging industries. Moreover, their relatively weak economic foundation highlights the significant outcomes of transformation and development efforts. Conversely, mature cities tend to maintain stable or slightly declining transformation performance. These cities often rely heavily on the resource industry, which has long held a dominant position. Consequently, they face challenges in generating strong transformation momentum, particularly when the available resources have not been fully depleted. The findings of scholars such as Zhang et al. (2020), Long et al. (2021) and Liu et al. (2021b) corroborate these observations. Liu et al. (2021b) emphasize that mature cities continue to experience high dependence on resources, making transformation efforts challenging. Long et al. (2021) argue that cities in different developmental stages exhibit variations in their transformation speed, further highlighting the complexity of the transformation process.

4.1.3 Gradual slowing down of the growth rate of resource-based cities with high transformation performance

Gansu faces challenges in the transformation of its resource-based cities due to the absence of inherent advantages. However, the province has managed to achieve rapid transformation performance through close coordination between its cities. On the other hand, Ningxia, being geographically distant from the resource-based cities of Shaanxi and Gansu, is less affected by their influence (Guo and Liu, 2022). As a result, the transformation performance in this region has not shown significant improvement. In contrast, resource-based cities in Shaanxi have generally exhibited high transformation performance over many years. This can be attributed to the province’s strong economic and technological foundations, as it serves as an outpost in the central and western regions of China. Cities like Xianyang, Yulin, and Baoji in Shaanxi have a long history of development and have made significant contributions to the region’s transformation efforts. Research conducted by Chen et al. (2018) supports these findings, indicating that Xianyang and Yulin experienced a period of transformation from 2001 to 2015 before entering a stage of even higher transformation performance. However, extending the analysis until 2019 reveals a decline in the transformation performance of these two cities.

4.2 Conclusions

4.2.1 The resource-based cities in Shaanxi, Gansu, and Ningxia demonstrated commendable transformation performance from 2010 to 2019

Despite large differences in the transformation performance of various resource-based cities and over time, these differences are shrinking.

4.2.2 The regions with high transformation performance are dynamic and constantly evolving

Over an extended period, the transformation performance of each city, influenced by changes in regional economic and social development, will continue to fluctuate, leading to shifts in areas with high transformation performance.

4.2.3 The transformation performance of resource-based cities varies greatly depending on the stage of development

The comprehensive scores of the transformation performance of growing, declining, and regenerative cities have all improved. The immediate effect of the transformation of mature cities is not obvious, and their performance has remained stable or decreased slightly.

4.2.4 Imbalances exist among the transition regions of resource-based cities, leading to a diffusion effect

In Shaanxi, cities with high transformation performance, particularly Xianyang and Yulin, serve as centers of transformation experience, technology, and knowledge that spread to surrounding resource-based cities. Similarly, in Gansu, cities with faster transformation performance are concentrated in the central part of the province. The diffusion effect contributes to the improvement of cities with weaker transformation performance. Shizuishan city in Ningxia has demonstrated relatively stable transformation performance over the years. It is steadily enhancing its transformation efforts and gradually emerging as a new center for transformation.

4.3 Recommendations

To improve the transformation performance of resource-based cities, the following suggestions are proposed for policy formulation:

4.3.1 Exploring differentiated management policies for resource-based cities

In order to ensure a steady improvement of transformation performance while maintaining reasonable variations between cities, it is crucial for each city to adopt customized economic and environmental protection policies that align with their respective local conditions. This approach will enable cities to uphold their high transformation performance in certain regions, while simultaneously supporting and nurturing cities with weaker transformation performance in other regions.

4.3.2 Building a dynamic and open transformation environment

Cities are intensifying their efforts to support key projects in mature and declining areas, as well as adjusting the industrial structure and strategy based on incentives for weak transformation performance. This includes fostering the growth of non-resource industries and facilitating industrial transfers from cities with successful transformation. It is important to note that these policies should be consistently evaluated and adapted to accommodate the changing landscape of resource-based cities.

4.3.3 Establishing the concept of transformation through regional cooperation

Governments at all levels should enhance communication and exchange regarding economic and environmental policies. This involves breaking down communication barriers between political units at the same level and across different levels, as well as guiding the expansion of the industrial chain and promoting the development of industries through the establishment of collaborative policies with shared goals and orientations. There is a need for improved cooperation and coordination among transformation performance cities in various regions, integrating their functions with other urban sectors.

4.3.4 Focusing on improving the business environment

Resource-based cities should proactively optimize the regional tax incentive system, aiming to attract investment and create a favorable environment for foreign enterprises and business growth. Alongside policy improvements, the government should leverage its own advantages and actively engage in “marketing” efforts for urban development. For example, supporting local enterprises to diversify and establish businesses relevant to urban development. Ultimately, these measures will aid resource-based cities in achieving diversified transformation and sustainable development.

4.3.5 Promoting enterprise innovation and improving the cooperation mechanisms between enterprises and universities and research institutes

Formulating incentive policies that encourage industry-university research cooperation: Implementing policies that provide incentives for collaborative research projects between industries, universities, and research institutes. Institutionalizing industry-university research cooperation through legislation: Creating a legal framework that formalizes and promotes collaboration between industries, universities, and research institutes. Guiding enterprises in adopting new growth methods: Encouraging businesses to embrace innovative approaches to growth. Encouraging diverse forms of industry-university research cooperation: Promoting various models of collaboration, including contract cooperation, technical alliances, joint establishment of research facilities, and mutual training initiatives. Promoting multi-level cooperation: Facilitating collaboration at different levels, including partnerships between enterprises, universities, and research institutes, as well as collaborations between different institutions. Facilitating talent exchange and improvement between universities, research institutes, and enterprises.

4.3.6 Establishing and improving a long-term mechanism for sustainable development of resource-based cities and a compensation mechanism for resource development

Adhering to market laws: Following market principles and regulations to guide the rational development of resources by various market entities. Implementation of legal, economic, and administrative measures: Establishing a comprehensive framework of laws, economic incentives, and administrative regulations that guide resource development in a sustainable manner. Resource compensation, ecological protection, and restoration: Placing the responsibility on resource developers to compensate for the environmental and ecological impact caused by their activities. Resource product price formation mechanism: Establishing a mechanism that reflects the true value of resources, taking into account factors such as scarcity, market demand and supply, environmental considerations, and the costs associated with ecological restoration.

4.3.7 Optimizing the talent training system

Optimizing talent training can attract high levels of research and development teams and professional talent. It can also encourage all kinds of talent to innovate and start businesses in the production and scientific research frontlines, activate the core elements of scientific and technological innovation, and promote creative intellectual activities for supporting the implementation of innovation strategies. At the same time, vocational skills training for transferred, laid-off, and migrant workers must be strengthened and the technical knowledge level of the labor market improved to meet the needs of urban development.

4.4 Limitations and scope for future research

This study contributes significant theoretical and practical implications by presenting a strategy for evaluating the performance of resource-based cities. However, there are certain limitations that should be addressed. Firstly, the evaluation index system could be further improved. It would be beneficial to categorize the index system according to the unique characteristics and challenges of resource-based cities. This would allow for the construction of more tailored and appropriate evaluation criteria for each type of city. Secondly, while the study focuses on resource-based cities in Shaanxi, Gansu, and Ningxia, it would be valuable to expand the research scope to include additional regions for a more comprehensive assessment and comparison. Alternatively, selecting a representative city from each stage of development and conducting a detailed analysis and comparison could provide deeper insights into the transformation process. Lastly, extending the time span of the study would offer a better understanding of the long-term urban transformation process. By observing and analyzing the performance of resource-based cities over an extended period, researchers could gain valuable insights into the dynamics and trends associated with urban transformation.
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