Construction and Evaluation of the Integrated Perception Ecological Environment Indicator (IPEEI) Based on the DPSIR Framework for Smart Sustainable Cities

Liu, Yingbing; Du, Wenying; Chen, Nengcheng; Wang, Xiaolei

doi:10.3390/su12177112

Cited by 14 publications

(10 citation statements)

References 29 publications

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“…The essence of the DPSIR framework is a causal chain of these five subsystems. Specifically, drivers exert pressures on regional land resources, resulting in changes in states of regional land resources, and subsequently leading to a series of impacts on human and regional land resources, which promote responses as feedback to the drivers [40,59,[62][63][64]. The interrelationship between the five subsystems of the DPSIR framework is illustrated in Figure 1.…”

Section: Responsementioning

confidence: 99%

Assessing Land Resource Carrying Capacity in China’s Main Grain-Producing Areas: Spatial–Temporal Evolution, Coupling Coordination, and Obstacle Factors

Jiang,

Tang,

et al. 2023

Sustainability

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The land resources in the main grain-producing areas (MGPAs) provide a solid foundation for grain production, and promotion of the sustainable utilization of land resources in these areas is crucial for ensuring national food security. To comprehensively assess the land resource carrying capacity (LRCC) in China’s MGPAs, we utilized the driver-pressure-state-impact-response (DPSIR) framework and applied the analytic hierarchy process (AHP) and entropy weight (EW) method to analyze the spatial–temporal evolution of LRCC in China’s MGPAs from 2000 to 2020. By establishing a coupling coordination model, we explored the coupling coordination relationships among LRCC subsystems and identified key factors hindering the balanced development of LRCC using an obstacle degree model. The research results indicate that the LRCC in China’s MGPAs generally shows an increasing trend with a continuously growing rate, with the LRCC in the southern regions surpassing that in the northern regions. The overall coupling coordination of LRCC demonstrates an increasing trend, although the growth rate is decreasing. The coupling coordination level of LRCC in the southern regions is generally higher than that in the northern regions, and the gap in coordination levels between various regions is narrowing. The state and pressure subsystems significantly influence the balanced development of LRCC. Indicators such as arable land area per capita, grain production yield per unit area of arable land, grain production yield per capita, forest land area per capita, and grassland area per capita play vital roles in the development of LRCC. Based on these findings, we have put forward targeted recommendations.

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Section: Responsementioning

confidence: 99%

Assessing Land Resource Carrying Capacity in China’s Main Grain-Producing Areas: Spatial–Temporal Evolution, Coupling Coordination, and Obstacle Factors

Jiang,

Tang,

et al. 2023

Sustainability

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“…An analysis of the literature indicates that works are published relatively rarely that contain the results of empirical studies on the actual evaluation of cities in terms of living conditions and QoL in smart sustainable cities. On the other hand, if such results are already published, they usually concern large metropolises located in the socalled "old EU-14" countries [24], Asia [25,26], or the United States [27].…”

Section: Introductionmentioning

confidence: 99%

Measuring and Assessing the Level of Living Conditions and Quality of Life in Smart Sustainable Cities in Poland—Framework for Evaluation Based on MCDM Methods

Brodny,

Tutak,

Bindzár

2024

Smart Cities

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The increasing degree of urbanization of the world community is creating several multidimensional challenges for modern cities in terms of the need to provide adequate living and working conditions for their residents. An opportunity to ensure optimal conditions and quality of life are smart sustainable cities, which integrate various resources for their sustainable development using modern and smart technological solutions. This paper addresses these issues by presenting the results of a study of the level and quality of living conditions in the 29 largest cities in Poland, an EU member state. This study used 35 indicators characterizing the six main areas of activity of the cities to assess the living conditions and quality of life in these cities. To achieve this purpose, an original research methodology was developed, in which the EDAS and WASPAS methods and the Laplace criterion were applied. The application of a multi-criteria approach to the issue under study made it possible to determine the levels of quality of life and living conditions in the studied cities for each dimension, as well as the final index of this assessment (Smart Sustainable Cities Assessment Scores). On this basis, a ranking of these cities was made. In addition, relationships between living conditions and quality of life and the levels of wealth and population of the cities were also assessed. The results showed a wide variation in the levels of living conditions and quality of life in the cities studied, as well as their independence from geographic location. Cities with higher GDP levels that were investing in innovation and knowledge-based development fared much better.

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“…Therefore, resources are more important, and when resources are depleted, the environment will be completely deteriorated. In recent years, domestic and foreign experts and scholars often use the DPSIR model to analyze the problem of sustainable development of agricultural resources and economy, are an evaluation model proposed by OECD in 1993, and were first adopted by the European Environment Agency to assess the environment [22][23][24]. Next, we analyze the sustainable development of the agricultural economy in Jiangsu Province (as shown in Figure 1) as an example.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Sustainable Development of an Agricultural Economy Based on the DPSIR Model

2022

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With today’s rapid economic growth, the role of agriculture in production and life has come to the forefront. Many problems in China’s agricultural economy have seriously constrained the vitality of Chinese agriculture. Therefore, it is necessary to further strengthen the study of an agricultural economy, inject new impetus into an agricultural development, develop targeted solutions, and lay a solid foundation for the sustainable development of China’s agricultural economy. Therefore, in this paper, based on the theory of sustainable development of an agricultural economy, this paper constructs a sustainable agricultural development index system in Jiangsu province from five aspects of the DPSIR model: driving force, pressure, state, impact and response and uses principal component analysis to make a comprehensive evaluation of the level of sustainable agricultural development in Jiangsu province from 2010 to 2016. The analysis results show that Jiangsu’s agricultural economy is overall better and steadily improving, and it has made a huge leap in the seven-year period from 2010 to 2016, showing better sustainability. We found that the state subsystem exposed some social problems after analyzing the subsystem, and if relevant measures are not taken in time to solve the emerged problems, it will affect the other subsystems and the sustainability of the agricultural economy.

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Construction and Evaluation of the Integrated Perception Ecological Environment Indicator (IPEEI) Based on the DPSIR Framework for Smart Sustainable Cities

Cited by 14 publications

References 29 publications

Assessing Land Resource Carrying Capacity in China’s Main Grain-Producing Areas: Spatial–Temporal Evolution, Coupling Coordination, and Obstacle Factors

Assessing Land Resource Carrying Capacity in China’s Main Grain-Producing Areas: Spatial–Temporal Evolution, Coupling Coordination, and Obstacle Factors

Measuring and Assessing the Level of Living Conditions and Quality of Life in Smart Sustainable Cities in Poland—Framework for Evaluation Based on MCDM Methods

Evaluation of Sustainable Development of an Agricultural Economy Based on the DPSIR Model

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