sustainability
Article Research on Sustainable Land Use Based on Production–Living–Ecological Function: A Case Study of Hubei Province, China
Chao Wei 1, Qiaowen Lin 2, Li Yu 3,* , Hongwei Zhang 3 , Sheng Ye 3 and Di Zhang 3
1 School of Public Administration, Hubei University, Wuhan 430062, China; [email protected] 2 School of Management and Economics, China University of Geosciences, Wuhan 430074, China; [email protected] 3 School of Public Administration, China University of Geosciences, Wuhan 430074, China; [email protected] (H.Z.); [email protected] (S.Y.); [email protected] (D.Z.) * Correspondence: [email protected]; Tel.: +86-185-7163-2717
Abstract: After decades of rapid development, there exists insufficient and contradictory land use in the world, and social, economic and ecological sustainable development is facing severe challenges. Balanced land use functions (LUFs) can promote sustainable land use and reduces land pressures from limited land resources. In this study, we propose a new conceptual index system using the entropy weight method, regional center of gravity theory, coupling coordination degree model and obstacle factor identification model for LUFs assessment and spatial-temporal analysis. This framework was applied to 17 cities in central China’s Hubei Province using 39 indicators in terms of production–living–ecology analysis during 1996–2016. The result shows that (1) LUFs showed an overall upward trend during the study period, while the way of promotion varied with different dimensions. Production function (PF) experienced a continuous enhancement during the study period. Living function (LF) was similar in this aspect, but showed a faster rising tendency. EF Citation: Wei, C.; Lin, Q.; Yu, L.; continued to increase during 1996–2011, but declined during 2011–2016. LUFs were higher in the Zhang, H.; Ye, S.; Zhang, D. Research east than in the west, and slightly higher in the south than in the north. The spatial coordination was on Sustainable Land Use Based on enhanced during the study period. (2) The overall level of coupling coordination degree continued to Production–Living–Ecological increase during 1996–2016, while regional difference declined obviously, indicating a good developing Function: A Case Study of Hubei trend. However, the absolute level was still not satisfactory. (3) The obstacle degree of PF was always Province, China. Sustainability 2021, dominant, and LF showed a downward trend, while EF showed an increasing trend. Benefit index 13, 996. https://doi.org/10.3390/su 13020996 (A2), Comfort index (B2) and Green index (C1) constituted the primary obstacle factor for each dimension. Added-value of high and new technology industry (A2-3) and land use intensity (A3-2) Received: 26 December 2020 were key factors restricting PF. Number of medical practitioner (B1-4) and internet penetration rate Accepted: 16 January 2021 (B2-3) were key factors restricting LF. Air quality rate (C3-1) and wetland coverage rate (C1-4) were Published: 19 January 2021 key factors restricting EF. This study can help to give a more detailed understanding of sustainable land use for the particularity of China from a land function perspective and provide lessons and Publisher’s Note: MDPI stays neutral suggestions for other developing countries in the world. with regard to jurisdictional claims in published maps and institutional affil- Keywords: production–living–ecological land (PLEL); land use function (LUFs); sustainable land iations. use; coupling coordination; Hubei Province; China
Copyright: © 2021 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. The world we live in is complex while policy makers and scientists prefer to reduce This article is an open access article complexity for the purpose of simplifying decision-making. However, reducing assessment distributed under the terms and to a single dimension may miss cross-linkages and eventually lead to poor decision- conditions of the Creative Commons making [1]. Land is a comprehensive system which is composed of economic, social and Attribution (CC BY) license (https:// ecological subsystems [2,3] and it is also the material basis and fountain of resources for creativecommons.org/licenses/by/ the country’s social and economic development, the place and environment for national 4.0/).
Sustainability 2021, 13, 996. https://doi.org/10.3390/su13020996 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 996 2 of 21
survival and various activities. Mankind uses it for a multitude of purposes, and for striving for sustainable development. Land use functions (LUFs) are defined as the private and public goods and services provided by different land uses that summarize the most relevant economic, environmental and social issues of a region [4], which was stimulated by the European project named “Sustainability Impact Assessment: Tools for Environmental Social and Effects of Multi- functional Land Use in Europe Regions (SENSOR)”. According to LUFs, land systems can be categorized into three classes (Figure1): production land (PL), living land (LL) and ecological land (EL), namely, production–living–ecological land (PLEL) [5,6].
Figure 1. Production–living–ecological land and production–living–ecological functions. (Source: made by authors).
According to the above figure, PL provides agricultural products, industrial products and service products as the leading function of the region, ensuring the survival and development of mankind. LL carries and maintains human settlements, mainly exerts the function of human habitation, consumption, leisure and entertainment. EL refers to the main function of providing ecological products and ecological services, playing an extremely important role in maintaining, regulating and safeguarding regional ecology. In short, PL is regarded as driving force, EL is foundation, LL is considered as the link of PLEL. The three aspects of PLEL are closely related and mutually transformed [7]. Due to the population explosion and inadequate land resources, the competitions among PLEL are becoming increasingly fierce. The long-lasting and rapid urbanization has led to the rapid expansion of construction land (LL), and a large number of cultivated land (PL) and ecological land (EL) around the town have been occupied [8]. Likewise, forest land and water bodies (EL) have been occupied by cultivated land (PL) for food production. There exist insufficient and contradictory situations during land use and social, economic and ecological sustainable development is facing severe challenges. Actually, land use competitions are the results of conflicts and compromises with different functions and relevant underlying objectives [9,10]. Contemporary people have started to realize that a piece of land not only provides economic production function, but also provides social and ecological functions [11]. PLEL exerts their dominant functions respectively. Moreover, PLEL also exerts their non-principal functions. As a result, func- tional superposition and multi-functions occur (Figure2). Multifunctional land use aims at maximizing the benefits obtained from a given parcel of land [12]. Gradually, land use management has changed the way from focusing on one single function to multi-functional land use. Therefore, multifunctional land use has become an important path to solving these conflicts and promoting efficient and sustainable land use [13]. Sustainability 2021, 13, 996 3 of 21
Figure 2. Functional superposition and multi-functions of land use. (Source: made by authors).
Researches of multifunctional land use concepts that originated from the agricultural sector, referring to the simultaneous provision of diverse output and the consequent sat- isfaction of multiple demands [14–17]. Then, researches extended from agriculture to economy, society and environment, as well as sustainable land use assessment [18–20], attracting widespread attentions from scientists and policy makers. To balance the three dimensions (economic, social and environmental) of sustainability, SENSOR project pro- posed a conceptual framework of regional sustainability assessment from the perspective of LUFs [4]. Each sustainability dimension is represented by three LUFs: Economic (Residen- tial and Industrial Services, Land-based Production, Infrastructure), Social (Work, Health and Recreation, Culture) and Environmental (Abiotic Resources, Provision of Habitat, Ecosystem Processes), giving nine land use functions in all. The SENSOR project greatly promoted the application of multifunctional land use methodology in the field of land use sustainability impact assessment. Scholars established a conceptual framework to assess Chinese LUFs during 1985–2005 [21]. Then, policy scenarios and detailed subdivisions of land utilization were taken into account by scholars [22]. Further, a methodological framework was presented based on the concept of LUFs to assess the impact of land use policies on sustainable development in developing countries [23]. However, despite the increase in publications on LUFs, more research might be completed on the connotation of sustainable land use [24]. Since the reform and opening up, China’s land space utilization has made remarkable achievements with relatively scarce land resource endowments [8,25]. The Report on the Government’s Work at the Fourth Session of the Twelfth National People’s Congress on 5 March 2016 emphasized that the objectives of land use in China should focus on integrated development for production, living, and ecology. Further, Commission of the European Community in the Impact Assessment Guidelines also stated that sustainable impact assessment should perform an integration of economic, environmental and social issues. Therefore, the purpose of land use is to pursue coordination of LUFs and realize sustainable land use. Thus, this paper proposed a conceptual index system for sustainable land use assessment in terms of the three aspects of LUFs of production–living–ecology. Then, this paper demonstrated the use of proposed LUFs assessment index system in 17 cities of Hubei Province in China as an example using entropy weight method, regional center of gravity theory, coupling coordination degree model and obstacle factor identification model. The results confirmed the situation that there is still a long way to go for the sustainable land use in China, and measures should be taken to realize the scientific development that emphasizes multi-dimensional coordination and overall progress. This paper is committed to promoting balanced LUFs, insisting that only comprehensive coordinated development of LUFs is the sustainable land use. Compared with the past research, this study can help to give a more detailed understanding of sustainable land use for the particularity of China and provide lessons and suggestions for other developing countries in the world. Sustainability 2021, 13, 996 4 of 21
2. Materials and Methods 2.1. Study Area and Data Source 2.1.1. Study Area Hubei Province (HP) is located in the central region of China, between the northern latitude of 29◦0105300–33◦604700 and the east longitude of 108◦2104200–116◦0705000. HP has various types of landforms, including mountains, hills, plains and lake areas with out- standing natural endowment advantages. The terrain of HP is roughly surrounded by mountains in the east, west and north, low in the middle, showing the incomplete basin slightly open to the south. There are 17 municipal administrative districts in HP (Figure3).
Figure 3. Location map of study area (source: made by authors).
As an important implementation area of China’s central rise strategy, HP has achieved rapid progress of urbanization and industrialization in recent years, its economic status in the country has been rising, and its comprehensive strength in regional development has been significantly improved. In 2019, HP’s regional gross domestic product (GDP) reached 4.58 trillion RMB, ranking 7/31 in China (except Hong Kong, Macao and Taiwan). The province’s urbanization rate reached 61.00%, higher than the national average (60.60%). The ecological environment quality of HP is generally good, and the river water, lake water, reservoir water, air quality and the level of urban environmental noise show a good trend. Therefore, it is of great practical significance to choose HP as a research area to conduct the study, and the case has a high reference value for other regions.
2.1.2. Data Sources and Data Pre-Processing Data in this study mainly included land use data and statistics data of economic, demographic, environmental and social development. Land use data were collected from Resource and Environment Data Cloud Platform (http://www.resdc.cn/), while specific statistics data were mainly derived from the Hubei Statistical Yearbook, Hubei Rural Statistics Yearbook, China Urban Statistics Yearbook and China Urban Construction Sustainability 2021, 13, 996 5 of 21
Statistics Yearbook. Further, it was supplemented by the regional statistical yearbook of the 17 cities (states, forest areas). At the same time, work reports and statistical bulletins of the Hubei provincial government and municipal governments, the statistics reports of the Natural Resources Department, Ecological Environment Department and other relevant departments were used to make up for the missing data in yearbooks. Data of different indicators may have different units and characteristics. Therefore, in order to eliminate the influence of dimension, magnitude, and positive and negative orien- tation, the numeric data needs pre-processing before using [26]. A standardization method of the data range was used to pre-process the original data. After the transformation, all the indicators’ values were transformed into normalized values with a numerical range from 0 to 1. ( 0 (Xi − Xmin)/(Xmax − Xmin)(positive indicator) Xi = (1) (Xmax − Xi)/(Xmax − Xmin)(negative indicator)
0 where Xi means the normalized value. Xi refers to the original value. Xmax and Xmin are the maximum and minimum values, respectively.
2.2. Construction of Three-Dimensional Indicator System Firstly, this study fully analyzed the connotation of sustainable land use based on LUFs. Secondly, this research compared and analyzed the evaluation indicators in relevant researches [27], then an initial evaluation indicator system was established. Thirdly, the initial indicator system was sent to 20 experts and scholars in the fields of land resource management, natural geography, regional development and planning for argumentation. The rules of argumentation were as follows. (1) If more than half of the experts consider an indicator to be unimportant or inappropriate, the indicator is eliminated. (2) If the correlation between the indicators is considered strong, the indicators are grouped or the indicator whose data are easily obtained is chosen. (3) The experts can also propose new indicators to be added to the existing indicator system and accept the next round of expert argumentation. (4) After three rounds of expert argumentation, indicators that more than 80% of the experts identified are included in the final indicator system. The final indicator system is shown in Table2.
Table 1. Evaluation indicator system and weight of land space utilization.
Level 1 Level 2 Level 3 Units Weight Indicators Indicators Indicators A1-1 Investment of fixed assets per unit of construction land 100 million 0.0413 area (+) RMB/km2 A1-2 Employment in secondary and tertiary industry per unit person/km2 0.0419 Input of construction land area (+) index (A1) A1-3 Input intensity of R&D Expenditure (+) % 0.0355 A1-4 Mechanical power of per unit of arable land area (+) kW/hectare 0.0108 A1-5 Primary production laborers per unit of arable land person/hectare 0.0127 area (+) Production 10,000 function A A2-1 Fiscal revenue per unit of construction land area (+) 0.0419 RMB/km2 A2-2 GDP of secondary and tertiary industry per unit of 100 million 0.0496 Benefit construction land area (+) RMB/km2 index (A2) A2-3 Added-value of high and new technology industry per 100 million 0.0851 unit of land area (+) RMB/km2 A2-4 Added-value of primary industry per unit of arable land 10,000 0.0092 area (+) RMB/hectare A2-5 Yield per unit of sown area (+) ton/hectare 0.0123 Sustainability 2021, 13, 996 6 of 21
Table 2. Cont.
Level 1 Level 2 Level 3 Units Weight Indicators Indicators Indicators A3-1 Construction land area per capita (+) m2 0.0378 Intensity A3-2 Development intensity of land space (+) % 0.0533 index (A3) A3-3 Multiple cropping index (+) dimensionless 0.019 A3-4 Effective irrigation index (+) dimensionless 0.0253 B1-1 Drinking water penetration rate (+) % 0.0103 Basic living B1-2 Fuel gas penetration rate (+) % 0.0061 index (B1) B1-3 Engel’s coefficient of urban family (−) dimensionless 0.0192 B1-4 Number of medical practitioner per 10,000 people (+) person 0.0399 B2-1 Urban housing area per capita (+) m2 0.0188 B2-2 Disposable income of households per capita (+) RMB 0.0203 Comfort B2-3 Internet penetration rate (+) % 0.046 Living index (B2) function B B2-4 Urban road area per capita (+) m2 0.0136 B2-5 Public transport per 10,000 people (+) unit 0.0248 B3-1 Registered unemployment rate in urban areas (−) % 0.0283 B3-2 Average rate of participation in basic pension insurance, Safety % 0.0263 basic medical care insurance and unemployment Insurance (+) index (B3) B3-3 Number of criminal case per 10,000 people (−) case 0.0099 B3-4 Ratio of urban income and rural income (+) dimensionless 0.0203 C1-1 Percentage of greenery coverage in built-up areas (+) % 0.0168 Green C1-2 park land area per capita (+) m2 0.0134 index (C1) C1-3 Forest coverage rate (+) % 0.0336 C1-4 Wetland coverage rate (+) % 0.0377 tce ※/10,000 C2-1 Energy consumption per unit GDP (−) 0.0307 Ecological RMB Threat − 2 function C C2-2 Discharge of industrial waste gas per unit of land area ( ) ton/km 0.0167 index (C2) C2-3 Discharge of waste water per unit of land area (−) ton/km2 0.0120 C2-4 Discharge of solid waste per unit of land area (−) ton/km2 0.0081 C3-1 Rate of good air quality (+) % 0.0335 Governance C3-2 Industrial wastewater discharge compliance rate (+) % 0.0159 index (C3) C3-3 Rate of multipurpose use of solid waste (+) % 0.0088 C3-4 Treatment rate of consumption wastes (+) % 0.0134 ※ tce stands for ton of standard coal equivalent.
2.3. Determine the Indicators’ Weight The information entropy could measure the disorder degree of system information, and reflect the amount of useful information of the data [28]. The entropy method relies on the discreteness of the data itself, and the smaller the entropy of an indicator, the greater the amount of information provided by the indicator and the greater the role it plays in the comprehensive evaluation. Accordingly, the indicator deserves a higher weight. Set the r11 r12 ... r1n r21 r22 ... r2n decision matrix R = r = R = , where r is the normalized ij m×n ...... ij rm1 rn2 ... rmn value of the ith object to the jth indicator. In this study, m = 17, n = 39. (1) Calculate the contribution of the ith object to the jth indicator.
m pij = rij/ ∑ rij (2) i=1 Sustainability 2021, 13, 996 7 of 21
(2) Calculate the entropy value of the jth indicator. The entropy value ej represents the total contribution of all the evaluation objects to the jth indicator.
1 m ej = − ∑ pijlnpij (3) lnm i=1
(3) Calculate the diversity coefficient of the jth indicator. The diversity coefficient (Dj) indicates the inconsistency degree of each evaluation object’s contribution under the jth indicator, and the greater the Dj is, the more important the jth indicator will be.
Dj = 1 − ej (4)
(4) Determine the weight coefficient.
n uj = Dj/ ∑ Dj (5) j=1
where uj means the weight of the jth indicator. Thus, the weights determined by entropy weight method can be obtained as U = (u, u2,... un), satisfies the condition n 0 ≤ uj ≤ 1 ≤ 1, ∑ uj = 1. j=1
2.4. Calculation of Land Use Functions Based on the normalized values and weights of index system, the values of level1 indicators were calculated by Formula (6).
n 0 0 F(x) = ∑ Xj × uj (6) j=1
Then the comprehensive function (F) was calculated by Formula (7).
F = aFp + bFl + cFe (7)
Fp, Fl and Fe refers to the value of production function (PF), living function (LF) and ecological function (EF), respectively, and a, b, c denotes the contribution of Fp, Fl and Fe, respectively.
2.5. The Improved Coupling Coordination Degree Model As a whole, comprehensive function can indeed reflect the general situation of land use sustainability in a region. However, the result is often the superposition of high-value dimension and low-value dimension, which may conceal the short board in a certain dimension. Therefore, while the comprehensive function of land use is improved, the coupling coordination among PF, LF and EF should be paid more attention. Coupling, which stems from the physics, describes the phenomenon by which two or more systems influence each other through interactive mechanisms [29,30]. So coupling can be used to identify the relationship among the three aspects of LUFs. The mathematic formula can be written as: 1 " # 3 Fp × Fl × Fe C = (8) 3 Fp + Fl + Fe The above model is concise and practical with obvious physical significance. However, the inadequacy of this model is that, once one subsystem’s value is 0, no matter whatever the other subsystems’ values are, the coupling degree is 0. This situation obviously doesn’t comply with the reality of the socioeconomic system [31]. Further, the values of coupling degree are distributed in a relative narrow range, which leads to the lack of hierarchy. Sustainability 2021, 13, 996 8 of 21
Hence, this study tries to deduce a new model that can overcome the above-mentioned problem based on the statistically coefficient of variation. (1) Based on the concept of coupling mechanism, in order to ensure the coupling among Fp, Fl and Fe, only need to ensure that the coefficient of variation (Cv) is minimum.
r 2 2 2 (Fp−F) +(Fl −F) +(Fe−F) Cv = 3 /F (9) F = Fp + Fl + Fe /3
(2) Formula deformation: v u u 6 × FpFl + Fl Fe + FpFe C = 2 − (10) v t 2 Fp + Fl + Fe
0 (3) In order to minimize Cv, C should be maximized. 6 × FpFl + Fl Fe + FpFe C0 = (11) 2 Fp + Fl + Fe
0 In general, 0 ≤ Cv ≤ 1, 1 ≤ C ≤ 2. (4) To make sure 0 ≤ C0 ≤ 1, build the function C00 : 6 × FpFl + Fl Fe + FpFe C00 = − 1 (12) 2 Fp + Fl + Fe
00 00 000 Further, the larger the value of C , the smaller Cv will be. Then transfer C to C