Evaluation of Regional Resources and Environmental Carrying Capacity in China: A Case Study of Shandong Province

Abstract

The evaluation of resources and environmental carrying capacity (RECC) is of great significance for achieving harmony between humans and resources and the environment to realize sustainable development. However, current research has not reached a consensus on the research objects, theories, and methods for RECC evaluation. Therefore, this study defined the research object of regional RECC evaluation and designed an evaluation process for regional RECC based on the mutation progression method developed from the mutation theory. Then, the RECCs of 16 cities in Shandong Province during 2013–2022 were calculated, and their temporal and spatial evolution characteristics were analyzed. The result shows that: (1) the research object of regional RECC evaluation is essentially the concentrated reflection of the interaction between resources, the environment, the economy, and society; (2) the process of “construct a multilevel evaluation index system–determine the mutation types of the evaluation index system–standardize the lowest level indexes–evaluate the comprehensive regional RECC” could provide reference for RECC evaluation; and (3) from 2013 to 2022, the RECC in Shandong Province showed a steady increasing trend, and the RECC in eastern and central areas in Shandong Province was relatively higher. By analyzing these results, we found that the natural background conditions, the mode of production and life, and the decisions of the central government are the important factors affecting regional RECCs.

1. Introduction

Resources and the environment are the foundation of human survival and development. As the world continues to progress, human socio-economic activities have profoundly influenced resources and the environment, leading to resource exhaustion and environmental deterioration. Now, in order to realize sustainable development, humans have realized the need to promote harmonious development with resources and the environment, and to consciously master the state of resources and the environment for sustaining humanity. RECC has generated great attention.

RECC is an important yardstick for measuring the relationship between human socio-economic activities, resources, and the environment. It can be understood as the capacity of resources and environmental elements within a given region to support human socio-economic activities at a given stage of development, with specific economic, technical, and production and living conditions [1]. It evolved from the concept of carrying capacity, which was initially a mechanical concept. At that time, carrying capacity attracted attention and research interest from disciplines such as ecology, and has taken on new connotations as it has been studied. Once it was expanded to the field of resources and the environment for research, the concept of RECC was formed. Therefore, early studies on RECC originated in the study of carrying capacity, which can be traced back to Malthus’ An Essay on the Principle of Population in 1798 [2] and to the logistic equation proposed by Verhulst, based on Malthus’ theory, in 1838 [3]. They are considered the ideological origins of the concept of carrying capacity. In the 20th century, ecologists Park and Burges proposed the idea that carrying capacity is the maximum number of individuals that can exist under certain conditions in 1921 [4]. The next year, Hawden and Palmer conducted research under specific pasture conditions. They concluded that carrying capacity is the maximum number of livestock the pasture can support without damaging the pasture [5]. After that, many scholars have carried out similar studies [6,7]. Although the contents of these studies differed, the connotations expressed were similar. Overall, these studies focused on the maximum number that the condition of resources the environment in a habitat can support for a species, without reducing the habitat’s ability to support the species’ population size in the future. Usually, the study of the maximum population size is referred to as research on ecological carrying capacity (ECC), which not only broadens the connotation of carrying capacity but also prompts human reflection on natural resources and the environment, and lays the foundation for future research on RECC. In the 1960s, amid growing concerns about resource scarcity, research on resource carrying capacity (RCC) gained prominence. Until the end of the 1960s, research on the RCC was basically a direct extension of the research on ECC. Since the late 1960s and early 1970s, discussions of Earth’s carrying capacity have received increasing attention due to resource and environmental issues arising from population growth and economic development. One example was Limits to Growth, published by Meadows et al. in 1972, which pointed out that rapid industrialization and an increasing floating population would push the Earth’s carrying capacity to its limit [8]. Since then, research on RCC has placed greater emphasis on the relationships between population, food, and resources. Resources and the environment are interdependent, and research on environmental carrying capacity (ECC) based on the relationship between population and environment extends research on ECC and RCC. To a certain extent, the global environmental damage and pollution since the 1970s have prompted research on ECC. Furthermore, RECC is generally considered to be a composite of RCC and ECC [9,10]. Since the 1970s and 1980s, with continuous in-depth development, research on the evaluation of RCC [11,12,13,14], ECC [15,16,17], and RECC [18,19,20] has been widely conducted. They have become some of the most frequently used methods for evaluating the degree of restriction on human activities. Subsequently, and until now, studies on RECC have focused on evaluating carrying capacity.

Research on RECC evaluation has undergone a transition from focusing on single resource elements or environmental elements, such as land resources [21,22,23,24,25,26], water resources [27,28,29,30,31,32], and atmospheric-environment carrying capacity evaluation [33,34,35], to the comprehensive carrying capacity of resources and the environment [26,36,37,38,39]. Correspondingly, the evaluation objects of RECC are also divided into two categories: one is a specific resource element or environmental element, and the other is the comprehensive RECC from the perspective of the overall region. Many scholars actively developed methods of combining theory with practice, such as ecological footprint [40,41,42,43], water footprint [44,45], virtual water [46,47,48], energy analysis [49,50,51], system dynamics [52,53,54], PSR [55,56], DPSIR [57,58], and multi-angle evaluation index systems [59,60,61], for RECC evaluations. These methods have been well applied in practice. For example, GFN calculated the ecological footprint of many countries to determine whether each country’s demand for resources and environment was overloaded [62]. Wang et al. used the SD model to predict and analyze the water resource carrying capacity of Urumqi city, which opened a new path for the prediction of water resource carrying capacity in China [63]. Hughey et al. used the PSR model as a framework to study the environmental situation in New Zealand [64]. These evaluation studies provided decision-making support based on the threshold for sustaining human society and the economy in a definite period and region, and at a definite level of resources and environmental status [65,66,67,68,69], and also provided an important basis for the timely grasp of regional RECC. Overall, there were various studies on RECC. It is generally believed that it is of great significance for us to better respond to changes in resources and the environment and to address their adverse impacts on human survival and development.

China, the world’s largest developing country, in the current context of deepening conflict between human development and resources and the environment, should adjust and optimize its high-resource-consumption, high-environmental-pollution development model. For this reason, the carrying capacity of China’s resources and environment for social and economic development has become a major concern. In 2013, the third plenary session of the 18th Communist Party of China Central Committee included “building monitoring and warning mechanisms for RECC” as one of the important tasks of the Central Reform in the new era in Decision on Major Issues Concerning Comprehensively Deepening Reforms [70]. This has sparked great interest among researchers in RECC, and the public has also paid increasing attention to RECC evaluation. In China, evaluation results are conducive to a comprehensive understanding of RECC and to a comprehensive analysis of the supply and feedback between resources, the environment, and human activities, thereby providing measures to coordinate the relationships between resource utilization, economic development, population growth, and environmental protection. The government has successively introduced a series of policies to strengthen the fundamental position of RECC evaluation [1,71,72]. Given this background, correctly evaluating and understanding the RECC of a region has become an important task for China. However, current research has not reached a consensus on the research objects, theories, and methods for RECC evaluation [73]. We believe that in order to make the RECC evaluation become one of the effective tools that can provide recommendations for regional policymakers and stakeholders, it is important to define the evaluation objects of regional RECC, and to ensure that the evaluation methods are simple and practical, the evaluation processes are scientific and clear, and data sources are authoritative and reliable. Considering all of these, this study, based on the existing research, analyzed the research object of regional RECC, combined with the mutation progression method developed from the mutation theory, to explore an evaluation process for regional RECCs. Shandong Province in China was used as a case study. This study will enrich the theory and practice of RECC evaluation and, at the same time, provide a solid scientific basis for improving the utilization efficiency of China’s resource environment and for formulating a reasonable, sustainable development strategy.

2. Study Area

Shandong Province is a coastal province in East China, the only demonstration province of the United Nations Biodiversity Finance Project in China, ranging from 34°22.9′~38°24.01′ N latitude and 114°47.5′~122°42.3′ E longitude (Figure 1). Its land area is 155,800 square kilometers, about 721.03 km long from east to west and 437.28 km wide from south to north, including mountains, hills, plains, lakes, and other landforms, and it has a temperate and monsoonal climate. It governs 16 cities: Jinan, Qingdao, Zibo, Zaozhuang, Dongying, Yantai, Weifang, Jining, Taian, Weihai, Rizhao, Binzhou, Dezhou, Liaocheng, Linyi, and Heze. By the end of 2022, Shandong Province’s regional GDP reached CNY 8,743,513 million, ranking third among all provinces in China, with a per capita GDP of CNY 86,034.573. In the same year, the permanent population of Shandong Province was 101.628 million, ranking second in China. It can be seen that Shandong Province, as a large province in terms of population and economy, faces significant pressure on RECC, which indicates that Shandong Province should scientifically and effectively evaluate RECC and develop plans for socio-economic development and resource environmental protection after mastering information on RECC evaluation.

3. Data Sources and Study Methods

3.1. Data Sources

This study comprehensively considered various indices that might affect the regional RECC. The data used to evaluate the RECC of Shandong Province from 2013 to 2022 were mainly raw data on the lowest-level indices. In order to ensure the authenticity and reliability of the data, they are obtained from the Statistical Yearbook of Shandong Province and Its 16 Cities from 2014 to 2023, using cities as the basic statistical unit.

3.2. Study Methods

Research on RECC usually takes a certain region as the research scope. A region is a unified organism, with complex interrelationships and interactions among resource, environmental, economic, and social elements. Meanwhile, according to the RECC concept [1], the research on regional RECC studies this organism. It can also be said that the regional RECC is a concentrated reflection of the interaction among resources, the environment, the economy, and society, which should be the research object of regional RECC evaluation; the states of these four elements have an indicative effect on it, and are constantly changing. These changes seem disordered, but they are actually within a certain range. Once the range is exceeded, the state of the RECC will change from one to another. Therefore, to master the state of the regional RECC, it is necessary to evaluate the concentrated reflection. Rene Thom proposed and systematically elaborated the mutation theory [74]. This theory studies how continuous, gradual changes cause mutations in states and strives to describe them using a unified mathematical method. Based on this, this study used the mutation progression method, derived from mutation theory, to evaluate regional RECC.

Learning from the basic steps of the mutation progression method, this study designed the following evaluation process for regional RECC (Figure 2).

3.2.1. Construction of an Evaluation Index System

The core and foundation of regional-RECC evaluation is the construction of an index system, and its appropriateness directly affects evaluation effectiveness. To objectively, scientifically, and comprehensively reflect the state of the regional RECC, the index system must embody the research object of RECC evaluation, which should be the concentrated reflection of the interaction between resources, the environment, the economy, and society. And the construction of the evaluation index system should include two tasks: designing the hierarchical relationship of indices and selecting specific indices. In the mutation progression method, each index is usually decomposed into at most four indices at the next level, and the indices at each level are sorted by importance. When evaluating, only the index data at the lowest level needs to be collected.

Based on the mutation progression method, combined with expert consultation and literature review, this study constructed the regional-RECC evaluation index system according to the principles of scientific nature, systematicity, hierarchy, quantifiability, and practicality (

Table 1. The evaluation index system ofregional RECC.

Target Layer	Criterion Layer	Index Layer	Basic Index Layer	Unit	Attribute
Regional RECC (X)	Resource utilization (xa)	Land resource carrying capacity (x1)	Cultivated land per capita (x11)	ha/ person	Positive correlation
			Construction land per capita (x12)	ha/ person	Moderate
			Land development intensity (x13)	%	Moderate
		Water resource carrying capacity (x2)	Water resources per capita (x21)	m3/ person	Positive correlation
			Water resource utilization rate (x22)	%	Moderate
			Amount of precipitation (x23)	mm	Positive correlation
		Energy consumption (x3)	Energy consumption per GDP (x31)	ton of standard coal/ CNY 10,000	Negative correlation
			Energy consumption per value added of industrial enterprises above the designated size (x32)	ton of standard coal/ CNY 10,000	Negative correlation
	Environmental safety (xb)	Impact of agricultural production (x4)	Consumption of pesticides (x41)	ton	Negative correlation
			Consumption of chemical fertilizer (x42)	10,000 tons	Negative correlation
			Consumption of plastic film (x43)	10,000 tons	Negative correlation
		Discharge of major pollutants (x5)	Discharge of COD (x51)	10,000 tons	Negative correlation
			Discharge of sulfur dioxide (x52)	10,000 tons	Negative correlation
			Discharge of nitrogen oxides (x53)	10,000 tons	Negative correlation
			Discharge of soot and dust (x54)	10,000 tons	Negative correlation
		Environmental improvement (x6)	Green coverage rate in built-up area (x61)	%	Positive correlation
			Afforestation area (x62)	ha	Positive correlation
			Utilization and disposal of hazardous waste (x63)	10,000 tons	Positive correlation
			Utilization and disposal of industrial solid waste (x64)	10,000 tons	Positive correlation
	Economic development (xc)	National economic development (x7)	GDP per capita (x71)	CNY/ person	Positive correlation
			Contribution rate of tertiary industry (x72)	%	Positive correlation
			Growth of total investments in fixed assets (x73)	%	Positive correlation
			Proportion of R&D in GDP (x74)	%	Positive correlation
		Resident life (x8)	Disposable income per capita (x81)	CNY/ person	Positive correlation
			Expense of household consumption per capita (x82)	CNY/ person	Positive correlation
	Social progress (xd)	Social stability (x9)	Urbanization rate (x91)	%	Positive correlation
			Urban population density (x92)	person/km2	Negative correlation
			Number of employed persons (x93)	10,000 persons	Positive correlation
			Number of persons participating in residents’ old-age insurance (x94)	10,000 persons	Positive correlation
		Infrastructure improvement (x10)	Density of road network (x101)	km/ km2	Positive correlation
			Density of water supply pipelines (x102)	km/ hectare	Positive correlation
			Gas penetration rate (x103)	%	Positive correlation
			Volume of passenger traffic (x104)	10,000 person-times	Positive correlation
		Health care (x11)	Number of health institutions per 10,000 persons (x111)	unit	Positive correlation
			Number of beds per 1000 persons (x112)	set	Positive correlation
			Number of medical technical personnel per 1000 persons (x113)	unit	Positive correlation
		Education, science, and technology development (x12)	Number of college students per 10,000 persons (x121)	person	Positive correlation
			Proportion of R&D personnel to the total population (x122)	%	Positive correlation

). This index system can be divided into four levels: target layer, criterion layer, index layer, and basic index layer. Among them, the target layer is the overall target of the index system, that is, the regional RECC; the criterion layer is the index established in accordance with the research object of RECC evaluation, reflecting the state of regional RECC from the four elements: resource utilization, environmental safety, economic development, and social progress; the index layer, based on the criterion layer, selects the index that reflects the essential characteristics of the four elements; and the basic index layer is a refinement of the index layer, which further explains the index layer.

3.2.2. Determination of the Mutation Types of the Evaluation Index System

The mutation progression method proposes four common mutation types: folding mutation, cusp mutation, swallowtail mutation, and butterfly mutation. The classification criteria for each type are that when a higher-level index is decomposed into one, two, three, or four lower-level indices, the higher-level index and its decomposed indices constitute a folding mutation, cusp mutation, swallowtail mutation, and butterfly mutation, respectively.

3.2.3. Standardization of the Lowest-Level Indices

According to the requirements of the mutation progression method, it is necessary to standardize the raw data for the lowest-level indices at the beginning of the evaluation to eliminate dimensional effects. This study adopts the fuzzy membership function method [75,76] to standardize these indices, and the standardized formulas for these three types of indices are as follows:

Positive indices:

$$\mathrm{B}\left({\mathrm{X}}_{\mathrm{ij}}\right)=\left\{\begin{array}{cc}1& {\mathrm{X}}_{\mathrm{ij}}\ge {\mathrm{X}}_{\mathrm{imax} }\\ {\displaystyle \frac{{\mathrm{X}}_{\mathrm{ij}}-{\mathrm{X}}_{\mathrm{imin}}}{{\mathrm{X}}_{\mathrm{imax}}-{\mathrm{X}}_{\mathrm{imin}}}}& {\mathrm{X}}_{\mathrm{imin}}{<\mathrm{X}}_{\mathrm{ij}}{<\mathrm{X}}_{\mathrm{imax}}\\ 0& {\mathrm{X}}_{\mathrm{ij}}\le {\mathrm{X}}_{\mathrm{imin} }\end{array}\right.$$

(1)

Negative indices:

$$\mathrm{B}\left({\mathrm{X}}_{\mathrm{ij}}\right)=\left\{\begin{array}{cc}1& {\mathrm{X}}_{\mathrm{ij}}\le {\mathrm{X}}_{\mathrm{imin}}\\ {\displaystyle \frac{{\mathrm{X}}_{\mathrm{imax}}-{\mathrm{X}}_{\mathrm{ij}}}{{\mathrm{X}}_{\mathrm{imax}}-{\mathrm{X}}_{\mathrm{imin}}}}& {\mathrm{X}}_{\mathrm{imin}}{<\mathrm{X}}_{\mathrm{ij}}{<\mathrm{X}}_{\mathrm{imax}}\\ 0& {\mathrm{X}}_{\mathrm{ij}}\ge {\mathrm{X}}_{\mathrm{imax}}\end{array}\right.$$

(2)

Moderate indices:

$$\mathrm{B}\left({\mathrm{X}}_{\mathrm{ij}}\right)=\left\{\begin{array}{cc}{\displaystyle \frac{2\left({\mathrm{X}}_{\mathrm{ij}}-{\mathrm{X}}_{\mathrm{imin}}\right)}{{\mathrm{X}}_{\mathrm{imax}}-{\mathrm{X}}_{\mathrm{imin}}}}& {\mathrm{X}}_{\mathrm{imin}}{<\mathrm{X}}_{\mathrm{ij}}{ <\mathrm{X}}_{\mathrm{i}0}\\ {\displaystyle \frac{{2(\mathrm{X}}_{\mathrm{imax}}-{\mathrm{X}}_{\mathrm{ij}})}{{\mathrm{X}}_{\mathrm{imax}}-{\mathrm{X}}_{\mathrm{imin}}}}& {\mathrm{X}}_{\mathrm{i}0}\le {\mathrm{X}}_{\mathrm{ij}}\le {\mathrm{X}}_{\mathrm{imax}\hspace{1em}\hspace{1em}}\\ 0& {\mathrm{X}}_{\mathrm{ij}}\le {\mathrm{X}}_{\mathrm{imin}}{ \mathrm{or} \mathrm{X}}_{\mathrm{ij}}\ge {\mathrm{X}}_{\mathrm{imax}}\end{array}\right.$$

(3)

where $\mathrm{B}\left({\mathrm{X}}_{\mathrm{ij}}\right)$ represents the standardized value of the i-th index in the j area in one evaluation year; ${\mathrm{X}}_{\mathrm{ij}}$ represents the raw data of the i-th index in the j area in one evaluation year; ${\mathrm{X}}_{\mathrm{imax}}$ represents the maximum value in the raw data of the i-th index over the research period within the study area; ${\mathrm{X}}_{\mathrm{imin}}$ represents the minimum value in the raw data of the i-th index over the research period within the study area; and ${\mathrm{X}}_{\mathrm{i}0}$ represents the most ideal raw data of the i-th index over the research period within the study area.

3.2.4. Evaluation of the Comprehensive Regional RECC

Based on the mutation progression method, the most important approach to evaluating regional RECC is to use the normalization formula for each mutation type in relevant calculations. The comprehensive evaluation is a process that calculates the mutation progression values for each index layer by layer until the overall target’s mutation progression value is determined.

As mentioned above, an index in a certain layer and its decomposition indices in the next layer constitute a mutation type; the calculation of mutation progression values is based on this combination. The calculation process requires two steps: (1) the mutation progression values of the decomposition indexes in the next layer are calculated using the normalization formulas of the mutation types they belong to, and the normalization formulas of different mutation types are shown in

Table 2. Normalization formulas of different mutation types.

Mutation Types	Normalization Formulas
Folding mutation	$x_a=\sqrt[]{a}$
Cusp mutation	$x_a=\sqrt[]{a}$, $x_b=\sqrt[3]{b}$
Swallowtail mutation	$x_a=\sqrt[]{a}$, $x_b=\sqrt[3]{b}$, $x_c=\sqrt[4]{c}$
Butterfly mutation	$x_a=\sqrt[]{a}$, $x_b=\sqrt[3]{b}$, $x_c=\sqrt[4]{c}$, $x_d=\sqrt[5]{d}$

; (2) the mutation progression value of the index from the previous layer is determined based on whether there is a complementary relationship between these decomposition indices, that is, whether they can compensate for each other’s shortcomings. This study assumes that the various components of regional RECC are complementary, so the mutation progression value of the index in the previous layer is taken as the average of the decomposition indices’ mutation progression values.

Here, $x_a$, $x_b$, $x_c$ and $x_d$ represent the mutation progression values of the decomposition indices; $a$, $b$, $c$, and $d$ represent the calculation bases.

In this study, the evaluation of the comprehensive regional RECC mainly consisted of three steps: First, combining the mutation types constituted by indices in the basic index layer and using the standardized values of the indices in the basic index layer as the calculation bases, we followed the above two calculation steps to calculate the mutation progression values of each index in the two layers over the research period in the study area. Then, based on the mutation types constituted by indices in the index layer and criterion layer and using the mutation progression values of indices in the index layer as the calculation bases, we calculated the new mutation progression values for the indices in the index layer and the mutation progression values for the indices in the criterion layer according to the two calculation steps mentioned above over the research period in the study area. Finally, similarly, by using the mutation progression values of the indices in the criterion layer calculated in the previous step as the calculation bases, and considering the mutation type constituted by the indices in the criterion layer and the total index, we can obtain the new mutation progression values of the indices in the criterion layer and the mutation progression value of the total target, and then we can proceed with the evaluation.

4. Results

4.1. Mutation Types of the Evaluation Index System

On the basis of the classification criteria for mutation types, the results for the mutation types of the evaluation index system are presented in

Table 3. The mutation types of the evaluation index system.

Target Layer	Criterion Layer	Mutation Types	Criterion Layer	Index Layer	Mutation Types	Index Layer	Basic Index Layer	Mutation Types
X	xa	Butterfly mutation	xa	x1	Swallowtail mutation	x1	x11	Swallowtail mutation
							x12
							x13
				x2		x2	x21	Swallowtail mutation
							x22
							x23
				x3		x3	x31	Cusp mutation
							x32
	xb		xb	x4	Swallowtail mutation	x4	x41	Swallowtail mutation
							x42
							x43
				x5		x5	x51	Butterfly mutation
							x52
							x53
							x54
				x6		x6	x61	Butterfly mutation
							x62
							x63
							x64
	xc		xc	x7	Cusp mutation	x7	x71	Butterfly mutation
							x72
							x73
							x74
				x8		x8	x81	Cusp mutation
							x82
	xd		xd	x9	Butterfly mutation	x9	x91	Butterfly mutation
							x92
							x93
							x94
				x10		x10	x101	Butterfly mutation
							x102
							x103
							x104
				x11		x11	x111	Swallowtail mutation
							x112
							x113
				x12		x12	x121	Cusp mutation
							x122

.

4.2. Standardized Processing of Raw Data

This study is based on the raw data for the lowest-level indices of Shandong Province from 2013 to 2022. Their standardized values were calculated using the standardized formulas and the attributes of the lowest-level indices. For moderate indices, the most ideal raw data for construction land per capita, land development intensity, and water resource utilization rate were set at 0.01 ha/person [77], 4.62% [78], and 40.00% [79], respectively. Considering the large number of standardized values in this study, we present the standardized values of Jinan, the capital city of Shandong Province (

Table 4. The standardized values of the lowest-level indices in Jinan from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x11	0.202	0.195	0.188	0.178	0.168	0.155	0.069	0.039	0.035	0.032
x12	1.955	1.956	1.955	1.960	1.955	1.957	1.925	1.951	1.948	1.945
x13	1.049	1.010	0.969	0.932	0.859	0.787	0.687	0.637	0.575	0.543
x21	0.258	0.079	0.128	0.193	0.101	0.218	0.139	0.208	0.372	0.314
x22	1.798	1.412	1.632	1.753	1.579	1.802	1.667	1.781	1.900	1.876
x23	0.436	0.157	0.309	0.693	0.322	0.572	0.254	0.419	0.727	0.597
x31	0.655	0.693	0.750	0.771	0.864	0.914	0.940	0.956	0.981	1.000
x32	0.688	0.726	0.771	0.790	0.887	0.948	0.960	0.980	0.981	1.000
x41	0.944	0.950	0.952	0.960	0.962	0.972	0.932	0.944	0.958	0.964
x42	0.544	0.556	0.564	0.573	0.599	0.620	0.621	0.646	0.668	0.690
x43	0.840	0.851	0.854	0.857	0.865	0.883	0.878	0.882	0.877	0.878
x51	0.393	0.416	0.399	0.902	0.911	0.913	0.917	0.737	0.767	0.800
x52	0.502	0.551	0.539	0.811	0.893	0.917	0.936	0.941	0.953	0.957
x53	0.715	0.770	0.758	0.835	0.871	0.894	0.901	0.869	0.871	0.881
x54	0.694	0.427	0.415	0.737	0.809	0.842	0.843	0.873	0.891	0.906
x61	0.261	0.313	0.336	0.358	0.388	0.388	0.440	0.396	0.463	0.478
x62	0.582	0.635	0.513	0.150	0.073	0.175	0.454	0.445	0.125	0.023
x63	0.049	0.068	0.049	0.166	0.227	0.235	0.221	0.209	0.293	0.249
x64	0.182	0.209	0.159	0.703	0.735	0.576	0.661	0.582	0.630	0.638
x71	0.301	0.344	0.367	0.397	0.440	0.488	0.489	0.515	0.589	0.620
x72	0.635	0.654	0.703	0.750	0.762	0.763	0.995	0.990	0.997	0.994
x73	0.965	0.897	0.869	0.861	0.858	0.801	0.845	0.718	0.829	0.715
x74	0.398	0.393	0.418	0.492	0.538	0.557	0.576	0.687	0.718	0.782
x81	0.402	0.413	0.326	0.387	0.604	0.687	0.695	0.735	0.826	0.878
x82	0.308	0.356	0.460	0.544	0.812	0.752	0.742	0.786	0.882	0.862
x91	0.683	0.694	0.738	0.780	0.810	0.854	0.829	0.892	0.913	0.916
x92	0.792	0.799	0.806	0.647	0.773	0.802	0.824	0.868	0.895	0.906
x93	0.554	0.572	0.578	0.580	0.551	0.504	0.647	0.578	0.582	0.579
x94	0.309	0.309	0.308	0.313	0.316	0.353	0.455	0.473	0.485	0.498
x101	0.364	0.378	0.399	0.385	0.378	0.364	0.490	0.517	0.524	0.524
x102	0.608	0.626	0.635	0.676	0.706	0.759	0.747	0.788	0.937	1.000
x103	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
x104	0.291	0.088	0.073	0.076	0.075	0.074	0.077	0.028	0.048	0.034
x111	0.524	0.553	0.582	0.574	0.580	0.617	0.821	0.825	0.825	0.846
x112	0.516	0.559	0.574	0.619	0.659	0.699	0.840	0.874	0.935	0.989
x113	0.443	0.502	0.541	0.578	0.629	0.694	0.840	0.887	0.949	1.000
x121	0.706	0.748	0.772	0.794	0.784	0.926	0.975	1.000	0.956	0.934
x122	0.621	0.696	0.753	0.785	0.831	0.846	0.647	0.630	0.756	0.952

).

4.3. Comprehensive Evaluation of RECC in Shandong Province

4.3.1. Temporal Evolution Characteristics of RECC

All mutation progression values are calculated in accordance with the mutation types of the evaluation index system and the standardized values of the lowest-level indices, and the time series of the mutation progression values for the total target showed the trends of RECC in Shandong Province during 2013–2022 (

Table 5. The mutation progression values of the total target in Shandong Province from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Jinan	0.977	0.975	0.977	0.981	0.982	0.985	0.983	0.984	0.986	0.985
Qingdao	0.975	0.976	0.972	0.977	0.979	0.980	0.978	0.981	0.981	0.978
Zibo	0.969	0.967	0.971	0.975	0.975	0.978	0.973	0.976	0.972	0.973
Zaozhuang	0.963	0.963	0.964	0.969	0.968	0.971	0.970	0.967	0.968	0.968
Dongying	0.963	0.953	0.966	0.966	0.970	0.974	0.967	0.969	0.970	0.972
Yantai	0.969	0.972	0.974	0.975	0.974	0.981	0.978	0.980	0.977	0.979
Weifang	0.959	0.962	0.964	0.966	0.970	0.969	0.972	0.975	0.975	0.973
Jining	0.960	0.966	0.969	0.973	0.975	0.976	0.973	0.976	0.978	0.977
Taian	0.971	0.971	0.972	0.976	0.975	0.978	0.974	0.978	0.973	0.980
Weihai	0.975	0.970	0.975	0.975	0.977	0.979	0.975	0.974	0.974	0.973
Rizhao	0.953	0.959	0.962	0.967	0.970	0.969	0.971	0.970	0.970	0.970
Linyi	0.957	0.965	0.966	0.970	0.973	0.970	0.968	0.971	0.972	0.973
Dezhou	0.963	0.961	0.965	0.969	0.971	0.974	0.971	0.973	0.976	0.977
Liaocheng	0.963	0.963	0.962	0.967	0.964	0.972	0.965	0.970	0.975	0.975
Binzhou	0.969	0.964	0.927	0.964	0.969	0.973	0.967	0.968	0.970	0.968
Heze	0.915	0.952	0.955	0.960	0.963	0.966	0.963	0.966	0.963	0.969
Average	0.963	0.965	0.965	0.971	0.972	0.975	0.972	0.974	0.974	0.974

). The time series of the mutation progression values of the criterion layer and index layer of the 16 cities in Shandong Province during 2013–2022 are displayed in

Table A1. The mutation progression values of the index layer in Jinan from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	0.904	0.898	0.892	0.885	0.875	0.862	0.805	0.780	0.769	0.762
x2	0.845	0.677	0.760	0.853	0.745	0.851	0.756	0.824	0.924	0.891
x3	0.846	0.866	0.892	0.901	0.945	0.969	0.978	0.985	0.992	1.000
x4	0.915	0.919	0.921	0.924	0.929	0.936	0.929	0.935	0.940	0.944
x5	0.818	0.811	0.804	0.945	0.961	0.966	0.969	0.944	0.951	0.957
x6	0.632	0.665	0.636	0.675	0.668	0.694	0.760	0.741	0.707	0.649
x7	0.808	0.814	0.825	0.842	0.856	0.862	0.888	0.891	0.914	0.914
x8	0.655	0.676	0.672	0.719	0.855	0.869	0.870	0.890	0.934	0.944
x9	0.851	0.856	0.863	0.853	0.868	0.877	0.900	0.908	0.914	0.917
x10	0.808	0.771	0.771	0.774	0.775	0.777	0.802	0.783	0.812	0.808
x11	0.781	0.803	0.817	0.827	0.841	0.862	0.936	0.945	0.958	0.972
x12	0.847	0.875	0.894	0.907	0.913	0.954	0.926	0.929	0.944	0.975

,

Table A2. The mutation progression values of the index layer in Qingdao from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	0.880	0.868	0.855	0.844	0.834	0.808	0.785	0.765	0.738	0.720
x2	0.738	0.740	0.559	0.677	0.763	0.791	0.628	0.859	0.834	0.647
x3	0.913	0.931	0.944	0.954	0.961	0.965	0.974	0.981	0.985	0.986
x4	0.887	0.887	0.889	0.892	0.897	0.903	0.909	0.911	0.915	0.917
x5	0.780	0.785	0.781	0.969	0.978	0.982	0.984	0.962	0.957	0.960
x6	0.644	0.655	0.578	0.505	0.528	0.585	0.625	0.546	0.512	0.564
x7	0.856	0.865	0.874	0.888	0.891	0.893	0.917	0.897	0.913	0.925
x8	0.699	0.735	0.756	0.802	0.847	0.890	0.921	0.939	0.984	0.997
x9	0.851	0.857	0.867	0.884	0.890	0.876	0.879	0.883	0.891	0.891
x10	0.660	0.606	0.598	0.598	0.599	0.580	0.580	0.562	0.584	0.558
x11	0.856	0.864	0.871	0.872	0.886	0.918	0.937	0.955	0.964	0.971
x12	0.719	0.731	0.739	0.766	0.770	0.818	0.816	0.818	0.885	0.911

,

Table A3. The mutation progression values of the index layer in Zibo from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	0.857	0.851	0.847	0.840	0.828	0.823	0.803	0.684	0.675	0.672
x2	0.825	0.653	0.755	0.806	0.716	0.883	0.857	0.858	0.665	0.680
x3	0.625	0.673	0.703	0.756	0.788	0.808	0.818	0.825	0.854	0.897
x4	0.949	0.954	0.957	0.959	0.966	0.968	0.976	0.981	0.983	0.984
x5	0.662	0.769	0.766	0.932	0.964	0.972	0.977	0.965	0.976	0.980
x6	0.757	0.756	0.773	0.765	0.744	0.718	0.729	0.831	0.654	0.646
x7	0.786	0.788	0.802	0.816	0.824	0.840	0.581	0.818	0.846	0.846
x8	0.593	0.630	0.662	0.707	0.748	0.789	0.821	0.838	0.882	0.899
x9	0.791	0.795	0.806	0.817	0.811	0.817	0.817	0.809	0.815	0.813
x10	0.732	0.595	0.591	0.594	0.599	0.601	0.599	0.579	0.571	0.532
x11	0.645	0.658	0.675	0.677	0.681	0.685	0.688	0.696	0.703	0.706
x12	0.541	0.547	0.557	0.567	0.578	0.610	0.580	0.586	0.653	0.664

,

Table A4. The mutation progression values of the index layer in Zaozhuang from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	0.955	0.951	0.946	0.942	0.936	0.932	0.865	0.857	0.851	0.858
x2	0.878	0.768	0.758	0.848	0.666	0.850	0.845	0.654	0.677	0.649
x3	0.679	0.720	0.777	0.792	0.817	0.827	0.832	0.849	0.864	0.886
x4	0.932	0.931	0.932	0.934	0.940	0.944	0.947	0.948	0.959	0.962
x5	0.906	0.912	0.913	0.970	0.981	0.984	0.986	0.975	0.981	0.979
x6	0.605	0.546	0.539	0.633	0.621	0.630	0.616	0.561	0.536	0.525
x7	0.671	0.701	0.712	0.727	0.732	0.686	0.698	0.722	0.744	0.760
x8	0.416	0.460	0.438	0.492	0.544	0.589	0.615	0.634	0.680	0.695
x9	0.732	0.743	0.759	0.774	0.785	0.781	0.781	0.723	0.733	0.736
x10	0.644	0.623	0.617	0.622	0.625	0.628	0.635	0.622	0.590	0.507
x11	0.411	0.430	0.450	0.477	0.497	0.514	0.525	0.539	0.545	0.552
x12	0.239	0.266	0.290	0.316	0.344	0.319	0.306	0.337	0.394	0.403

,

Table A5. The mutation progression values of the index layer in Dongying from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	0.876	0.861	0.854	0.858	0.852	0.851	0.729	0.722	0.635	0.744
x2	0.860	0.251	0.787	0.751	0.756	1.020	0.820	0.785	0.944	0.889
x3	0.893	0.902	0.918	0.918	0.927	0.932	0.937	0.948	0.959	0.970
x4	0.941	0.944	0.950	0.959	0.970	0.985	0.993	0.997	0.999	1.000
x5	0.922	0.927	0.926	0.977	0.986	0.989	0.989	0.985	0.987	0.987
x6	0.557	0.592	0.563	0.566	0.629	0.622	0.621	0.641	0.565	0.588
x7	0.697	0.811	0.829	0.802	0.847	0.808	0.754	0.840	0.852	0.860
x8	0.624	0.667	0.695	0.738	0.782	0.824	0.852	0.870	0.913	0.927
x9	0.398	0.419	0.420	0.380	0.481	0.455	0.457	0.452	0.468	0.401
x10	0.438	0.412	0.414	0.426	0.431	0.435	0.430	0.404	0.421	0.423
x11	0.205	0.243	0.266	0.222	0.238	0.281	0.302	0.310	0.334	0.339
x12	0.432	0.448	0.463	0.455	0.478	0.433	0.433	0.461	0.521	0.567

,

Table A6. The mutation progression values of the index layer in Yantai from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	0.989	0.986	0.983	0.981	0.976	0.974	0.906	0.904	0.900	0.903
x2	0.622	0.798	0.799	0.710	0.568	0.849	0.699	0.838	0.646	0.668
x3	0.916	0.928	0.948	0.954	0.964	0.969	0.973	0.970	0.979	0.985
x4	0.600	0.557	0.648	0.646	0.677	0.724	0.762	0.778	0.786	0.793
x5	0.770	0.792	0.801	0.955	0.968	0.973	0.975	0.961	0.965	0.967
x6	0.858	0.822	0.799	0.796	0.734	0.769	0.822	0.760	0.674	0.676
x7	0.792	0.807	0.813	0.828	0.828	0.832	0.826	0.837	0.851	0.871
x8	0.613	0.650	0.667	0.717	0.764	0.804	0.828	0.845	0.889	0.907
x9	0.798	0.813	0.824	0.835	0.854	0.862	0.862	0.865	0.868	0.867
x10	0.625	0.578	0.578	0.580	0.585	0.566	0.544	0.541	0.548	0.545
x11	0.757	0.753	0.751	0.751	0.755	0.767	0.778	0.787	0.794	0.795
x12	0.702	0.696	0.679	0.684	0.689	0.695	0.657	0.710	0.748	0.812

,

Table A7. The mutation progression values of the index layer in Weifang from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	0.989	0.985	0.981	0.977	0.972	0.965	0.868	0.859	0.853	0.859
x2	0.743	0.686	0.707	0.757	0.782	0.582	0.800	0.870	0.861	0.668
x3	0.808	0.828	0.852	0.872	0.879	0.889	0.876	0.898	0.915	0.928
x4	0.423	0.520	0.491	0.389	0.526	0.597	0.634	0.649	0.676	0.697
x5	0.634	0.673	0.720	0.941	0.954	0.963	0.966	0.950	0.957	0.955
x6	0.676	0.678	0.699	0.650	0.642	0.686	0.706	0.731	0.600	0.613
x7	0.736	0.756	0.771	0.785	0.780	0.778	0.740	0.794	0.820	0.820
x8	0.519	0.561	0.570	0.623	0.671	0.717	0.738	0.755	0.803	0.820
x9	0.751	0.764	0.778	0.792	0.802	0.825	0.832	0.842	0.850	0.864
x10	0.665	0.614	0.609	0.615	0.620	0.628	0.631	0.605	0.578	0.564
x11	0.874	0.862	0.857	0.865	0.870	0.892	0.913	0.927	0.941	0.951
x12	0.530	0.533	0.544	0.562	0.560	0.547	0.551	0.607	0.687	0.694

,

Table A8. The mutation progression values of the index layer in Jining from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	1.021	1.018	1.013	1.010	1.005	1.000	0.956	0.915	0.913	0.919
x2	0.745	0.736	0.776	0.844	0.841	0.854	0.731	0.882	0.965	0.870
x3	0.817	0.843	0.873	0.890	0.901	0.908	0.921	0.931	0.945	0.954
x4	0.742	0.745	0.739	0.741	0.757	0.785	0.804	0.818	0.828	0.837
x5	0.498	0.703	0.764	0.950	0.962	0.965	0.965	0.949	0.951	0.952
x6	0.579	0.669	0.821	0.671	0.675	0.665	0.669	0.680	0.619	0.652
x7	0.685	0.706	0.725	0.743	0.755	0.756	0.712	0.739	0.768	0.743
x8	0.454	0.501	0.485	0.540	0.588	0.634	0.653	0.669	0.714	0.730
x9	0.758	0.775	0.794	0.819	0.825	0.834	0.837	0.830	0.836	0.846
x10	0.603	0.589	0.583	0.592	0.595	0.614	0.607	0.592	0.581	0.572
x11	0.813	0.806	0.811	0.814	0.834	0.844	0.862	0.869	0.883	0.882
x12	0.419	0.439	0.451	0.472	0.487	0.472	0.434	0.501	0.555	0.543

,

Table A9. The mutation progression values of the index layer in Taian from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	0.996	0.993	0.989	0.987	0.983	0.980	0.914	0.911	0.913	0.921
x2	0.794	0.715	0.745	0.842	0.756	0.853	0.738	0.920	0.677	0.893
x3	0.810	0.832	0.865	0.877	0.896	0.903	0.912	0.918	0.925	0.938
x4	0.908	0.905	0.907	0.908	0.914	0.927	0.930	0.931	0.937	0.940
x5	0.842	0.857	0.856	0.974	0.979	0.982	0.983	0.972	0.968	0.968
x6	0.694	0.694	0.696	0.621	0.623	0.648	0.663	0.629	0.561	0.600
x7	0.748	0.762	0.774	0.783	0.787	0.779	0.723	0.768	0.720	0.800
x8	0.483	0.526	0.510	0.561	0.610	0.654	0.682	0.700	0.744	0.758
x9	0.751	0.762	0.776	0.788	0.797	0.795	0.792	0.794	0.799	0.802
x10	0.645	0.628	0.631	0.639	0.641	0.563	0.653	0.639	0.641	0.613
x11	0.632	0.636	0.641	0.651	0.659	0.673	0.681	0.689	0.700	0.707
x12	0.494	0.509	0.517	0.538	0.532	0.554	0.521	0.569	0.599	0.640

,

Table A10. The mutation progression values of the index layer in Weihai from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	1.003	0.997	0.992	0.989	0.982	0.978	0.947	0.940	0.935	0.932
x2	0.872	0.585	0.799	0.757	0.806	0.896	0.707	0.628	0.656	0.635
x3	0.892	0.906	0.923	0.937	0.949	0.956	0.967	0.977	0.981	0.985
x4	0.920	0.922	0.926	0.930	0.932	0.936	0.940	0.942	0.945	0.948
x5	0.950	0.956	0.954	0.990	0.996	0.998	0.998	0.989	0.991	0.992
x6	0.553	0.552	0.542	0.509	0.517	0.507	0.527	0.457	0.394	0.343
x7	0.798	0.820	0.838	0.857	0.864	0.841	0.814	0.847	0.870	0.831
x8	0.621	0.666	0.680	0.730	0.778	0.821	0.845	0.855	0.900	0.912
x9	0.706	0.654	0.649	0.663	0.671	0.668	0.662	0.673	0.682	0.679
x10	0.594	0.543	0.541	0.537	0.542	0.543	0.545	0.524	0.507	0.503
x11	0.424	0.438	0.439	0.444	0.434	0.452	0.468	0.478	0.481	0.479
x12	0.500	0.518	0.553	0.570	0.580	0.570	0.571	0.587	0.660	0.677

,

Table A11. The mutation progression values of the index layer in Rizhao from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	1.047	1.043	1.036	1.031	1.021	1.016	0.923	0.912	0.909	0.903
x2	0.899	0.776	0.818	0.835	0.858	0.660	0.816	0.731	0.680	0.660
x3	0.262	0.442	0.480	0.538	0.584	0.585	0.587	0.636	0.684	0.682
x4	0.937	0.949	0.965	0.969	0.972	0.978	0.984	0.986	0.990	0.992
x5	0.917	0.892	0.891	0.970	0.976	0.982	0.984	0.969	0.976	0.981
x6	0.587	0.587	0.594	0.653	0.677	0.710	0.752	0.677	0.635	0.616
x7	0.663	0.699	0.721	0.743	0.760	0.784	0.773	0.812	0.823	0.828
x8	0.404	0.457	0.464	0.517	0.567	0.615	0.637	0.650	0.693	0.707
x9	0.648	0.654	0.667	0.695	0.706	0.714	0.711	0.708	0.710	0.709
x10	0.592	0.582	0.576	0.586	0.592	0.606	0.615	0.603	0.565	0.563
x11	0.099	0.202	0.322	0.335	0.369	0.407	0.419	0.426	0.452	0.458
x12	0.299	0.354	0.361	0.385	0.430	0.442	0.447	0.495	0.523	0.528

,

Table A12. The mutation progression values of the index layer in Linyi from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	1.030	1.026	1.021	1.017	1.011	1.007	0.921	0.908	0.909	0.927
x2	0.637	0.785	0.809	0.891	0.895	0.625	0.614	0.684	0.669	0.642
x3	0.786	0.809	0.873	0.864	0.886	0.900	0.902	0.909	0.922	0.931
x4	0.620	0.640	0.656	0.659	0.678	0.711	0.753	0.764	0.786	0.794
x5	0.510	0.718	0.735	0.919	0.945	0.948	0.948	0.938	0.946	0.944
x6	0.759	0.761	0.772	0.675	0.744	0.750	0.744	0.698	0.637	0.655
x7	0.695	0.710	0.725	0.738	0.747	0.754	0.701	0.759	0.773	0.782
x8	0.394	0.451	0.409	0.473	0.530	0.549	0.606	0.627	0.675	0.690
x9	0.805	0.817	0.834	0.845	0.854	0.818	0.826	0.846	0.851	0.854
x10	0.638	0.588	0.554	0.584	0.597	0.606	0.616	0.592	0.552	0.543
x11	0.808	0.823	0.831	0.833	0.878	0.902	0.922	0.933	0.949	0.956
x12	0.323	0.348	0.366	0.377	0.403	0.408	0.381	0.430	0.482	0.519

,

Table A13. The mutation progression values of the index layer in Dezhou from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	1.064	1.060	1.057	1.053	1.050	1.045	1.026	1.026	1.029	1.023
x2	0.902	0.574	0.776	0.764	0.719	0.824	0.669	0.762	0.933	0.850
x3	0.796	0.820	0.847	0.869	0.891	0.901	0.912	0.907	0.925	0.950
x4	0.761	0.762	0.765	0.782	0.817	0.820	0.830	0.842	0.850	0.859
x5	0.758	0.787	0.793	0.960	0.971	0.971	0.974	0.964	0.968	0.967
x6	0.747	0.739	0.739	0.678	0.688	0.688	0.654	0.583	0.507	0.580
x7	0.663	0.689	0.702	0.719	0.726	0.747	0.743	0.784	0.762	0.817
x8	0.300	0.377	0.366	0.427	0.481	0.529	0.562	0.580	0.628	0.645
x9	0.680	0.709	0.724	0.738	0.772	0.777	0.746	0.740	0.736	0.744
x10	0.682	0.637	0.633	0.632	0.633	0.631	0.634	0.612	0.579	0.567
x11	0.593	0.601	0.611	0.619	0.648	0.668	0.680	0.683	0.690	0.701
x12	0.305	0.327	0.342	0.365	0.381	0.418	0.433	0.468	0.498	0.529

,

Table A14. The mutation progression values of the index layer in Liaocheng from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	1.049	1.045	1.041	1.038	1.030	1.025	0.946	0.945	0.945	0.952
x2	0.869	0.726	0.669	0.785	0.440	0.835	0.653	0.792	0.955	0.863
x3	0.744	0.774	0.810	0.808	0.844	0.876	0.867	0.873	0.896	0.912
x4	0.799	0.798	0.808	0.813	0.817	0.825	0.840	0.853	0.857	0.863
x5	0.787	0.799	0.811	0.954	0.967	0.974	0.979	0.968	0.970	0.968
x6	0.724	0.735	0.723	0.673	0.757	0.709	0.674	0.527	0.592	0.576
x7	0.684	0.697	0.709	0.722	0.732	0.712	0.611	0.764	0.779	0.786
x8	0.290	0.356	0.293	0.372	0.436	0.490	0.522	0.542	0.591	0.606
x9	0.634	0.683	0.726	0.748	0.763	0.764	0.771	0.740	0.747	0.753
x10	0.659	0.622	0.621	0.616	0.647	0.655	0.598	0.638	0.652	0.642
x11	0.643	0.659	0.663	0.683	0.710	0.721	0.736	0.743	0.743	0.745
x12	0.358	0.362	0.376	0.377	0.391	0.380	0.339	0.414	0.428	0.442

,

Table A15. The mutation progression values of the index layer in Binzhou from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	1.050	1.046	1.041	1.036	1.029	1.022	0.746	0.719	0.664	0.544
x2	0.908	0.623	0.813	0.822	0.717	0.894	0.794	0.787	0.923	0.943
x3	0.779	0.688	0.000	0.278	0.527	0.576	0.651	0.702	0.748	0.782
x4	0.898	0.899	0.896	0.900	0.907	0.923	0.930	0.935	0.942	0.949
x5	0.686	0.724	0.799	0.876	0.932	0.947	0.964	0.964	0.962	0.966
x6	0.642	0.674	0.761	0.776	0.790	0.848	0.885	0.787	0.709	0.708
x7	0.759	0.768	0.776	0.784	0.784	0.743	0.745	0.815	0.834	0.839
x8	0.469	0.512	0.531	0.581	0.627	0.654	0.680	0.696	0.742	0.755
x9	0.658	0.673	0.670	0.684	0.692	0.676	0.672	0.646	0.650	0.672
x10	0.620	0.588	0.581	0.584	0.588	0.591	0.589	0.568	0.537	0.510
x11	0.462	0.483	0.493	0.497	0.506	0.517	0.534	0.545	0.575	0.591
x12	0.431	0.435	0.458	0.465	0.455	0.438	0.443	0.488	0.538	0.552

,

Table A16. The mutation progression values of the index layer in Heze from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
x1	1.059	1.056	1.052	1.046	1.039	1.034	0.957	0.952	0.953	0.951
x2	0.733	0.790	0.807	0.824	0.779	0.817	0.711	0.811	0.697	0.783
x3	0.772	0.794	0.815	0.837	0.856	0.867	0.880	0.892	0.922	0.935
x4	0.589	0.545	0.587	0.591	0.599	0.674	0.723	0.748	0.760	0.768
x5	0.767	0.789	0.803	0.939	0.959	0.965	0.964	0.958	0.961	0.967
x6	0.563	0.611	0.619	0.567	0.593	0.618	0.693	0.651	0.538	0.536
x7	0.563	0.598	0.616	0.640	0.648	0.615	0.565	0.607	0.544	0.693
x8	0.000	0.245	0.263	0.353	0.428	0.486	0.521	0.548	0.599	0.613
x9	0.664	0.710	0.740	0.770	0.789	0.800	0.808	0.794	0.808	0.813
x10	0.634	0.612	0.606	0.612	0.623	0.627	0.610	0.532	0.367	0.470
x11	0.654	0.672	0.687	0.722	0.783	0.804	0.815	0.820	0.826	0.835
x12	0.064	0.213	0.235	0.268	0.263	0.219	0.149	0.277	0.367	0.390

,

Table A17. The mutation progression values of the criterion layer in Jinan from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.952	0.930	0.943	0.955	0.943	0.956	0.934	0.939	0.950	0.945
xb	0.928	0.932	0.928	0.950	0.952	0.956	0.962	0.959	0.957	0.952
xc	0.883	0.890	0.892	0.907	0.937	0.941	0.948	0.953	0.967	0.969
xd	0.940	0.941	0.944	0.944	0.948	0.953	0.961	0.961	0.967	0.969

,

Table A18. The mutation progression values of the criterion layer in Qingdao from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.940	0.940	0.911	0.928	0.939	0.938	0.912	0.940	0.932	0.903
xb	0.919	0.921	0.912	0.926	0.931	0.940	0.946	0.934	0.929	0.937
xc	0.906	0.916	0.923	0.936	0.945	0.954	0.965	0.963	0.975	0.980
xd	0.923	0.919	0.920	0.924	0.926	0.927	0.929	0.928	0.937	0.935

,

Table A19. The mutation progression values of the criterion layer in Zibo from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.918	0.899	0.916	0.926	0.916	0.938	0.932	0.910	0.885	0.891
xb	0.926	0.942	0.944	0.964	0.967	0.965	0.968	0.978	0.961	0.960
xc	0.863	0.872	0.884	0.897	0.908	0.920	0.849	0.924	0.939	0.942
xd	0.893	0.880	0.883	0.886	0.887	0.891	0.889	0.886	0.892	0.888

,

Table A20. The mutation progression values of the criterion layer in Zaozhuang from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.948	0.937	0.941	0.953	0.930	0.955	0.944	0.918	0.921	0.921
xb	0.938	0.931	0.931	0.949	0.950	0.952	0.951	0.944	0.943	0.942
xc	0.783	0.805	0.802	0.821	0.836	0.833	0.843	0.854	0.871	0.879
xd	0.818	0.823	0.831	0.840	0.847	0.846	0.846	0.841	0.846	0.838

,

Table A21. The mutation progression values of the criterion layer in Dongying from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.953	0.844	0.942	0.938	0.938	0.971	0.925	0.920	0.922	0.939
xb	0.936	0.941	0.939	0.946	0.957	0.959	0.960	0.963	0.954	0.957
xc	0.844	0.887	0.898	0.900	0.921	0.918	0.908	0.935	0.947	0.951
xd	0.727	0.736	0.742	0.727	0.753	0.751	0.755	0.753	0.768	0.760

,

Table A22. The mutation progression values of the criterion layer in Yantai from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.942	0.967	0.969	0.957	0.936	0.975	0.944	0.962	0.936	0.940
xb	0.885	0.875	0.893	0.911	0.912	0.926	0.939	0.934	0.927	0.929
xc	0.870	0.882	0.888	0.902	0.912	0.921	0.924	0.930	0.942	0.951
xd	0.903	0.899	0.899	0.901	0.905	0.905	0.901	0.905	0.909	0.913

,

Table A23. The mutation progression values of the criterion layer in Weifang from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.949	0.943	0.947	0.955	0.958	0.930	0.943	0.952	0.951	0.927
xb	0.806	0.835	0.837	0.834	0.868	0.890	0.901	0.904	0.896	0.901
xc	0.831	0.847	0.854	0.870	0.879	0.889	0.882	0.901	0.917	0.921
xd	0.897	0.892	0.894	0.899	0.901	0.906	0.909	0.913	0.917	0.918

,

Table A24. The mutation progression values of the criterion layer in Jining from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.956	0.957	0.964	0.974	0.974	0.975	0.953	0.966	0.977	0.967
xb	0.842	0.885	0.909	0.916	0.921	0.926	0.930	0.932	0.927	0.932
xc	0.798	0.817	0.819	0.838	0.853	0.864	0.856	0.867	0.885	0.881
xd	0.876	0.879	0.882	0.889	0.893	0.896	0.893	0.897	0.902	0.901

,

Table A25. The mutation progression values of the criterion layer in Taian from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.957	0.948	0.955	0.968	0.958	0.971	0.946	0.969	0.938	0.969
xb	0.937	0.938	0.938	0.944	0.946	0.951	0.954	0.949	0.941	0.946
xc	0.825	0.840	0.839	0.855	0.868	0.875	0.865	0.882	0.877	0.903
xd	0.873	0.874	0.877	0.883	0.884	0.878	0.886	0.889	0.893	0.894

,

Table A26. The mutation progression values of the criterion layer in Weihai from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.976	0.937	0.968	0.963	0.970	0.981	0.952	0.940	0.944	0.940
xb	0.935	0.936	0.935	0.935	0.937	0.937	0.940	0.930	0.920	0.912
xc	0.873	0.889	0.898	0.913	0.925	0.927	0.924	0.935	0.949	0.941
xd	0.840	0.829	0.831	0.834	0.836	0.837	0.838	0.839	0.844	0.844

,

Table A27. The mutation progression values of the criterion layer in Rizhao from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.901	0.918	0.928	0.938	0.945	0.918	0.923	0.916	0.914	0.910
xb	0.938	0.937	0.941	0.958	0.962	0.967	0.973	0.963	0.960	0.959
xc	0.777	0.803	0.811	0.832	0.850	0.868	0.870	0.884	0.896	0.900
xd	0.748	0.782	0.804	0.814	0.826	0.835	0.837	0.841	0.842	0.843

,

Table A28. The mutation progression values of the criterion layer in Linyi from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.939	0.961	0.970	0.978	0.980	0.944	0.928	0.937	0.936	0.936
xb	0.840	0.877	0.883	0.897	0.911	0.919	0.926	0.923	0.921	0.924
xc	0.783	0.805	0.797	0.819	0.837	0.844	0.842	0.864	0.878	0.884
xd	0.876	0.876	0.877	0.883	0.892	0.890	0.891	0.897	0.898	0.901

,

Table A29. The mutation progression values of the criterion layer in Dezhou from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.981	0.937	0.969	0.969	0.964	0.978	0.955	0.967	0.991	0.982
xb	0.905	0.908	0.909	0.926	0.935	0.936	0.934	0.926	0.918	0.930
xc	0.742	0.776	0.777	0.800	0.818	0.836	0.844	0.860	0.865	0.884
xd	0.843	0.846	0.850	0.856	0.865	0.871	0.869	0.869	0.868	0.871

,

Table A30. The mutation progression values of the criterion layer in Liaocheng from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.969	0.953	0.948	0.963	0.911	0.974	0.935	0.955	0.977	0.968
xb	0.913	0.916	0.918	0.931	0.942	0.939	0.939	0.922	0.931	0.930
xc	0.745	0.772	0.753	0.785	0.807	0.816	0.794	0.844	0.861	0.866
xd	0.844	0.849	0.857	0.862	0.871	0.872	0.863	0.872	0.876	0.877

,

Table A31. The mutation progression values of the criterion layer in Binzhou from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.978	0.929	0.651	0.894	0.921	0.948	0.896	0.895	0.906	0.886
xb	0.908	0.918	0.936	0.948	0.957	0.967	0.974	0.966	0.959	0.960
xc	0.824	0.838	0.845	0.860	0.871	0.865	0.871	0.895	0.909	0.913
xd	0.833	0.835	0.837	0.840	0.842	0.839	0.841	0.839	0.843	0.846

and

Table A32. The mutation progression values of the criterion layer in Heze from 2013 to 2022.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
xa	0.956	0.965	0.969	0.972	0.967	0.972	0.946	0.960	0.947	0.960
xb	0.850	0.849	0.861	0.872	0.879	0.899	0.917	0.916	0.905	0.907
xc	0.375	0.700	0.713	0.753	0.779	0.785	0.778	0.799	0.790	0.841
xd	0.787	0.833	0.841	0.854	0.862	0.859	0.845	0.857	0.847	0.866

in Appendix A.

Based on comparing the mutation progression values for each city in Shandong Province in 2013 and 2022, we found that the trends in RECC across cities can be divided into two categories: increases and decreases. Then, by analyzing changes in mutation progression values for each year and city between 2013 and 2022, we further divided the trends into three types. The first type is a steady increase, which refers to two or fewer decreases in values compared with the previous year during the period (Figure 3). These cities include Jinan, Qingdao, Weifang, Jining, Rizhao, Linyi, Dezhou and Heze. The second type is a fluctuating increase, which shows that there were three or more times the decreases in values compared with the previous year, including Zibo, Zaozhuang, Yantai, Taian, and Liaocheng (Figure 4). The third type is a fluctuating decrease: there was more than three times the increase in values compared with the previous year during the decreasing process, including Weihai and Benzhou (Figure 5). The average mutation progression value of RECC in Shandong Province during 2013–2022 showed a steady increase. In these cities, the highest RECC was Jinan in 2021 (0.986), and the lowest was Heze in 2013 (0.915), with an average of 0.970. From the perspective of the evaluation index system, the reasons for the high RECC in Jinan are its outstanding construction land per capita, water resource utilization rate, afforestation area, contribution rate of tertiary industry, density of water supply, number of beds per 1000 persons, and number of college students per 10,000 persons. The reasons for the low RECC in Heze are its low GDP per capita, low contribution rate of tertiary industry, low disposable income per capita, low expense on household consumption per capita, low urbanization rate, low density of water supply, low number of college students per 10,000 persons, and low proportion of R&D personnel to the total population. The average RECC showed a maximum value of 0.975 in 2018 and a minimum value of 0.963 in 2013, and remained at 0.974 from 2020 to 2023, indicating that the level of RECC in Shandong Province has been relatively stable in recent years.

4.3.2. Spatial Distribution Characteristics of RECC

The spatial distribution of RECC levels during 2013–2022 is shown in Figure 6. The natural breaks method was used to classify the RECC level into five grades, with the first grade indicating the lowest and the fifth the highest. Overall, the RECC in eastern and central Shandong Province was relatively higher than in other parts of the province. From 2013 to 2022, the RECC in the western region showed an overall upward trend, with Heze recording the largest increase, from 0.915 in 2012 to 0.969 in 2022, with an increase of 5.902%. In central and eastern regions, except for Weihai and Benzhou, the RECC also showed an overall upward trend, with Weihai showing the largest decrease, from 0.975 in 2012 to 0.973 in 2022, a decrease of 0.205%.

5. Discussion

To better evaluate regional RECC in China, this study established an evaluation index system and calculated the RECC of Shandong Province for 2013–2022 using the mutation progression method.

From the perspective of temporal evolution characteristics, the average mutation progression value of RECC in Shandong Province during 2013–2022 showed a trend of first increasing, then decreasing, and then increasing again. It increased until 2018, decreased in 2019, and then increased gradually. From the mutation progression values of the 16 cities in 2013 and 2018 (Table 5), the overall RECC of each city increased during those 6 years, although there were some increases or decreases during the period. This finding may be explained as follows. Shandong has always held a pivotal position in Chinese history and is a huge province in the economic, social, ecological, cultural, and political landscape of modern China. Especially after the conclusion of the 18th National Congress of the Communist Party of China in 2012, Shandong Province was the first province inspected by the General Secretary. Subsequently, in order to conscientiously implement the major strategic initiatives of the congress, Shandong Province released the Major Function Oriented Zone Planning in 2013, which clearly stated that the development direction of the region should be based on its RECC, thus achieving harmonious development of resources, the environment, the economy, and society [80]. Since then, Shandong Province has changed the way it pursues socio-economic development, at the cost of excessive resource consumption and environmental destruction. In addition, since 2013, Shandong Province has released a series of policies such as the Notice on Implementing the 2013 Municipal Government’s Responsibility Target for Cultivated Land Protection [81], Opinions on Further Promoting Land Conservation and Intensive Use [82], Notice on the Implementation of the Strictest Assessment Method for Water Resources Management System in Shandong Province [83], The 13th Five Year Plan for Ecological Environment Protection in Shandong Province [84], and Action Plan for the Construction of Basic Public Service System in Shandong Province (2013–2015) [85]. The implementation of these policies has enabled Shandong Province to achieve a series of achievements in optimizing resource utilization, consolidating environmental security, promoting economic development, and advancing social progress [86], as well as enhancing its RECC; therefore, its RECC has been increasing since 2013. This shows that the decisions of the central government are important factors affecting regional RECCs.

Then, the mutation progression values of the 16 cities in Shandong Province in 2019 all decreased compared to 2018, which is also a unified characteristic of the RECC of the 16 cities in Shandong Province in the time series, as shown in Figure 3, Figure 4 and Figure 5. The first of the big causes is that the change in the identified standard of cultivated land in the third national land resource survey [87] and the strict restrictions of construction land based on the urban growth boundary [88] resulted in a decrease in the land resource carrying capacity (Appendix A). The second main reason is due to the amount of precipitation and the local water resources decreasing significantly compared with the previous year [89], the water resource carrying capacity decreased (Appendix A). And the decline in national economic development caused by changes in GDP accounting [90] is the third main reason (Appendix A). These indicate that the natural background conditions and the decisions of the central government are the important factors affecting regional RECCs. Still, the RECC in Shandong Province has increased again after 2019 and has remained relatively stable since then. However, the increase is not due to the four aspects of resource utilization, environmental safety, economic development, and social progress all being improved: the increase in the discharge of major pollutants caused by regional production and living activities, as well as the overall reduction in afforestation area, have led to a decrease in the environmental safety level of many cities over the three years. However, the government’s support for the tertiary industry, employment, social security, health care, education, science and technology, etc., in these three years [91,92,93] has improved the economic development and social progress of Shandong Province, making up for the decrease in RECC caused by the decrease in environmental safety. This indicates that the mode of production and life and the decisions of the central government are the important factors affecting regional RECCs.

From the perspective of the spatial distribution characteristics of RECC in Shandong Province, the RECC in eastern and central Shandong Province is relatively higher than that in the other parts of the province. This finding may be explained as follows. The Spatial Planning of National Land in Shandong Province has made a major contribution to all cities in the province. It is determined in the planning that the national-level urbanized areas in Shandong Province are concentrated in the east. As is well known, in China, these areas already have a strong carrying capacity or need to increase it. The high RECC in central Shandong is due to the provincial capital, Jinan, which is also a national-level urbanized area, driving the development of surrounding areas, many of which have become provincial-level urbanized areas. It is not unique; Cheng et al. also found that regional RECCs are influenced by their main function in their evaluation study in 31 provinces of China [94]. This once again shows that the central government’s decisions are important factors affecting regional RECCs.

This study has achieved some results, but it still faces several limitations. First, selecting the research scale is crucial for studying RECC. In this study, to ensure the accessibility, continuity, and integrity of the data, we selected 16 provincial-level cities in Shandong Province for research, and the results were obtained according to the actual situation. If we can collect relevant data at the micro-scale in the future, the research results will be more accurate. Second, there is currently no unified, ready-made index system for evaluating regional RECC. From a practical perspective, the data of the evaluation index system constructed in this study is relatively easy to obtain. The evaluation index system can be supplemented or modified in subsequent work. When the data of some indices cannot be publicly obtained, cross-departmental collaboration can be considered to obtain them, such as obtaining indices reflecting soil quality from the agricultural sector, to more comprehensively evaluate the regional land carrying capacity. In addition, future research can also make full use of GIS spatial-analysis technology to obtain index data, such as interpreting remote sensing images to obtain the percentage of forest cover, to more comprehensively evaluate the regional environmental status.

6. Conclusions

This study defined the research object of regional RECC evaluation and introduced the mutation progression method into the evaluation. Based on this method, this study developed an evaluation index system and elaborated on the detailed evaluation process of regional RECC. An empirical study was conducted in Shandong Province. The main conclusions are as follows:

1. According to the research scope and concept of RECC, the core of regional RECC is resources, the environment, the economy, and society. Therefore, this study clarifies that the research object of regional RECC evaluation is essentially the concentrated reflection of the interaction between resources, the environment, the economy, and society. Based on this, an evaluation index system reflecting regional resource utilization, environmental safety, economic development, and social progress was constructed. This evaluation index system consists of four levels, fully reflecting the core of regional RECC, and it combines the four components of regional RECC with the comprehensive regional RECC. It provided a good idea and reference for evaluating the state of RECC across different regions and also expanded the research system for regional RECC.

2. Based on the mutation progression method developed from this theory, this study proposes an evaluation process of “construct a multilevel evaluation index system–determine the mutation types of the evaluation index system–standardize the lowest level indexes–evaluate the comprehensive regional RECC“ for RECC evaluation, which enriches the method for evaluating the regional RECC. Regions can evaluate their RECCs through this process to understand their current state. The application of this evaluation process can effectively assist local governments in conducting RECC evaluations, helping formulate and optimize policies and regulations related to RECC.

3. The main findings of the evaluation are as follows: (a) The RECC in Shandong Province showed a steady increasing trend during 2013–2022, with the lowest RECC in Heze in 2013 and the highest RECC in Jinan in 2021. (b) From the perspective of spatial distribution, the RECC in eastern and central Shandong Province was relatively higher than in other areas during 2013–2022. (c) Based on RECC evaluations in Shandong Province, we found that the natural background conditions, the mode of production and life, and the decisions of the central government are the important factors affecting regional RECCs.