An evaluation model for urban carrying capacity: A case study of China's mega-cities

Wei, Yigang; Huang, Changqing; Jing, Li; Xie, Lei

doi:10.1016/j.habitatint.2015.10.025

Cited by 172 publications

(82 citation statements)

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“…By building a multilevel indicator system, a comprehensive evaluation method can be used to evaluate the regional carrying capacity [42]. Two common indicator systems are as follows: one assumes that the standards of resources, environment, economy, society and other criteria are additive [43,44]; the other aims to build a "pressure-state-response"(PSR) [45] or "driving force-pressure-state-impact-response" (DPSIR) [46] model as a system layer or distinguish between pressure indicators and support indicators [42,47].…”

Section: Introductionmentioning

confidence: 99%

“…It is not conducive to the establishment of a unified early warning mechanism of RECC [47]. (2) Most studies divide the RECC system into resources criterion, environment criterion, human society criterion and calculate the comprehensive indicators [43,44]. These indicators cannot distinguish the regional resource and environmental status and the effects of human activities on the systems (e.g., the economic pressure and the governance measures).…”

Section: Introductionmentioning

confidence: 99%

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Resources and Environmental Pressure, Carrying Capacity, And Governance: A Case Study of Yangtze River Economic Belt

et al. 2020

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The analysis of the relationship between regional resources and environment and human activities plays an important role in sustainable regional development. This study proposes the pressure-capacity-governance (PCG) model, an analytic framework for the assessment of the resources and environmental pressure (REP), carrying capacity (RECC) and governance (REG) levels over a large watershed scale, with the Yangtze River Economic Belt (YREB) as the study area. A limiting factor analysis is used to recognize the limiting factors of the regional RECC. The coupling analysis of resources and environmental pressure-capacity-governance identifies the regional potential and utilization direction. The research results are as follows. (1) The REP, RECC and REG levels of the YREB exhibit spatial differences. The REPs of the upper reaches are lower than those of the lower reaches, which does not match the RECC but matches the REG levels.(2) The proportions of unused land, water resources, and atmospheric environmental quality are the main limiting factors of the regional RECC. (3) The PCG analysis framework is used as the basis to divide the YREB into several subareas to analyse the resources and environmental potential carrying capacity and utilization direction of different types of region. This research may provide decision-making references for regional sustainable development at the large watershed scale.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resources and Environmental Pressure, Carrying Capacity, And Governance: A Case Study of Yangtze River Economic Belt

et al. 2020

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“…Recently, there has been a growth of interest in indicator-based sustainability assessments for cities (Michael, Noor, & Figueroa, 2014;Mori & Yamashita, 2015;Shen & Zhou, 2014;Wei, Huang, Lam, & Yuan, 2015;Wei, Huang, Li, & Xie, 2016;Wong, 2015;Yigitcanlar, Dur, & Dizdaroglu, 2015). The indicators following the International Urban Sustainability Indicators List proposed in (Shen, Ochoa, Shah, & Zhang, 2011) include economic characteristics such as income per capita, social and cultural dimensions including unemployment rate, income differentiation rate in the form of a Gini coefficient and higher education level, and, finally, a wide range of ecological-economic or environmental dimensions, including the share of green space, CO 2 emissions, average particulate matter (PM 10 ) concentrations, water use per capita per day, waste generation per capita per day and recycling rates.…”

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confidence: 99%

Global urban sustainability assessment: A multidimensional approach

Shmelev

Shmeleva

2018

Sustainable Development

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A new strategic direction for greening our cities and making them smart to reduce the environmental impact of their performance, increase employment and economic viability, and to enhance the quality of life requires a thorough assessment of sustainability and smart urban performance. This paper considers a database of over 90 global cities including London, New York, Hong Kong, San Francisco, Los Angeles, Sao Paolo, Rio de Janeiro, Buenos Aires, Paris, Berlin, Stockholm, Moscow, Beijing, Seoul, Singapore, Shanghai, Sydney and Tokyo exploring linkages between different sustainability and smart city dimensions. To assess urban sustainability performance, this study applied a multi‐criteria approach using a panel of 20 indicators to a set of 57 global cities. The assessment comprised important aspects of energy transitions, focusing on the drivers of CO2 emissions in cities, including the share of coal in the energy mix, public transport and cycling patterns, waste recycling, the water‐energy nexus, as well as the role of smart and creative economy. The results show that San Francisco leads in economic and environmental priorities, and Stockholm leads in social and smart city priorities. Seoul consistently performs very successfully across the whole spectrum of indicators. We devote considerable attention to the strategies, policies and performance of the leading cities, namely, San Francisco, Stockholm and Seoul. This assessment could be a valuable tool for policy‐makers and investors, and could help identify linkages between different sustainability dimensions, as well as investment opportunities in cities with sustainability potential.

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“…Recently, studies carried out by Yale University, USA, and WHO, have ranked Delhi as the "worst" polluted city based on an environmental performance index (Hsu and Zomer, 2014). It was observed that increase in vehicular activity as resulted in deterioration of urban air quality in both developed and developing countries (Miller et al, 2006;Ravindra et al, 2015;Wei et al, 2016).…”

Section: Extreme Air Pollution Event At Designated Area In Megacitiesmentioning

confidence: 99%

Extreme Events of Reactive Ambient Air Pollutants and their Distribution Pattern at Urban Hotspots

Gulia

Nagendra

Khare

2017

Aerosol Air Qual. Res.

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The occurrence of extreme events of air pollutant concentrations at urban hotspots is a routine phenomenon, particularly during the winter season. However, extreme events of reactive air pollutants are more frequent during the summer season. The assessment of air pollution extreme events will provide a platform to formulate an effective and efficient hotspot urban air quality management plan. The statistical distribution model (SDM) is widely used to describe the average as well as extreme air pollutant concentration in a more organized and efficient manner. In the present study, the best fit SDM has been evaluated for hourly average PM 2.5 and NO 2 concentrations at one of the busiest traffic intersections in Delhi city (air pollution hotspot 1: APH-1) and for PM 2.5 at one of the heavily trafficked road corridors in Chennai city (air pollution hotspot 2: APH -2). The SDMs were developed for different seasons to evaluate the impacts of climatic conditions on the air pollution events. Results indicate that NO 2 concentrations were best fitted with lognormal and log logistic distribution models respectively, for winter and summer seasons at APH-1. However, lognormal distribution was best fitted to PM 2.5 concentration of winter and summer seasons at both APHs.

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An evaluation model for urban carrying capacity: A case study of China's mega-cities

Cited by 172 publications

References 23 publications

Resources and Environmental Pressure, Carrying Capacity, And Governance: A Case Study of Yangtze River Economic Belt

Resources and Environmental Pressure, Carrying Capacity, And Governance: A Case Study of Yangtze River Economic Belt

Global urban sustainability assessment: A multidimensional approach

Extreme Events of Reactive Ambient Air Pollutants and their Distribution Pattern at Urban Hotspots

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