Emergy-based ecological footprint analysis for a mega-city: The dynamic changes of Shanghai

Pan, Hengyu; Zhuang, Minghua; Geng, Yong; Wu, Fei; Dong, Huijuan

doi:10.1016/j.jclepro.2018.11.064

Cited by 71 publications

(32 citation statements)

References 71 publications

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“…In a certain sense, it corrects the shortcomings of only paying attention to the quantity of land in the traditional model. Compared with previous evaluations of the 3DEF model, developed regions [16,28,40] are more unsustainable, which is inconsistent with the actual nature of the social and economic development, while using net primary production or emergy [6,42] for calculating EF and EC still ignores the impacts of social and economic factors. The introduction of EC intensity as part of the carrying capacity plays an important role in reducing the research gap.…”

Section: Methods Comparison and Policy Suggestionsmentioning

confidence: 97%

“…Gernot [5] modified an EF model with the substitution of fossil energy by renewable energy carriers for assessing sustainable energy supplies. An emergy-based EF framework was proposed regarding the ability of environmental services to mitigate or eliminate impact of emissions [6]. Field surveys were carried out in rural areas based on an EF model in Reference [7].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Ecological Carrying Capacity Based on Revised Three-Dimensional Ecological Footprint Model in Inner Mongolia, China

et al. 2019

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Under the concept of green development, accurately mapping ecological carrying capacity to effectively evaluate regional sustainability has already become an important issue in China. This study introduced ecological carrying capacity intensity (ECintensity) based on the revised three-dimensional ecological footprint (3DEF) model to describe the temporal–spatial patterns of three-dimensional ecological carrying capacity (EC3D) in Inner Mongolia in 2010–2016 and to explore factors affecting socioeconomic sustainable development. The results showed that ecological footprint size (EFsize) differed between cities/leagues but changed little during the study period. Ecological footprint depth (EFdepth) far exceeded the original value of 1.00. Ecological carrying capacity (EC) varied in cities/leagues, while ECintensity increased slowly with stronger potential for regional development. Three-dimensional ecological deficits (ED3D) of cities/leagues were divided into five categories: Hohhot, Hulunbuir and Banyannur were in larger ecological surplus; Hinggan was in slight surplus; Baotou, Chifeng, Tongliao, Ulanqab, Xilin Gol and Erdos were in slight deficit; Wuhai was in stronger deficit; and Alxa was in severely intense deficit. Woodland of cities/leagues was continuously in slight ecological surplus, while cropland and grassland had crucial impacts on deficit. There was a significant positive linear correlation between gross domestic product (GDP) and footprint, while a negative correlation was seen with deficit. These results would help coordinate resource utilization and industrial structure adjustment in Inner Mongolia.

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Section: Methods Comparison and Policy Suggestionsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Assessing the Ecological Carrying Capacity Based on Revised Three-Dimensional Ecological Footprint Model in Inner Mongolia, China

et al. 2019

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“…A negative causal relationship exists between economic growth and ecological footprints [13]. Moreover, large cities tend to have large ecological deficits, extremely large ecological footprints, and poor coordination between ecological and economic systems, all of which entails considerable ecological challenges [27]. Cities have features such as advanced public facilities, comprehensive medical and service institutions, and job opportunities that exceed those of adjacent villages and towns, and these features tend to attract people from elsewhere.…”

Section: Description Of Preliminary Influencing Criteria Of Ecocity Dmentioning

confidence: 99%

Conceptualization and Development of a DFuzzy Model for Low-Carbon Ecocities

2019

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The Industrial Revolution has enabled mechanization to lead the manufacturing industry into a new era of explosive economic growth. Mass production through the use of machines has improved the overall societal prosperity in industrialized countries. The great and continuous demand of mechanization, economic growth, and energy consumption has resulted in the excessive use of natural resources and the mining of natural energy deposits. Challenges such as serious environmental pollution, ecological damage, and climate change have been identified as some of the critical issues for sustainable development. Therefore, this paper proposes a Delphi and Fuzzy (DFuzzy) model in order to institute low-carbon ecocities. DFuzzy is a scientific decision-making model with quantitative multiple attributes and artificial intelligence. Twelve specialists from the Delphi experts were interviewed, including chief executive officers (CEOs) of industries, management committee members, and senior academics, all of whom have 15 years of experience in urban planning practice. After a three-round Delphi process with 17 criteria that were taken from the literature, four criteria of the DFuzzy model were recognized by experts: policy norms, resident cooperation, pollution prevention and control, and ecological reserves. The practical application of the DFuzzy model took three areas as examples: the Baiyun District and Conghua District of Guangzhou City, and the Dahu Community of Kaohsiung City. Through the experts’ consensus in the two-round Delphi process, the four criteria, as the input basis, demonstrated the objective quantitative calculation function of the DFuzzy model and also indicated that the model established in this study provides a reference for evaluating low-carbon ecocities.

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“…Esse indicador representa a quantidade de área necessária para prover o total de recursos consumidos por uma determinada população, bem como, a área total de terra biologicamente produtiva necessária para absorver os resíduos gerados por esses mesmos indivíduos [7]. Assim, a PE reflete de forma direta a capacidade de carga dos sistemas ambientais, trazendo à tona as características espaciais da apropriação de um ecossistema e o estado das interações entre esse ecossistema e o homem [8]. Os dados de geração de resíduos sólidos urbanos no município de estudo e sua respectiva gravimetria foram obtidos através do Plano Estadual de Resíduos Sólidos do Estado de Alagoas [10].…”

Section: Introductionunclassified

Geração de resíduos sólidos urbanos no semiárido brasileiro: análise de sustentabilidade a partir da aplicação da pegada ecológica

et al. 2019

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A grande urbanização e o crescimento significativo da população mundial têm levado a um aumento considerável na geração de resíduos sólidos urbanos (RSU). A destinação final desses materiais é motivo de preocupação por parte da comunidade internacional, principalmente por seu potencial de degradação ambiental. Nesse sentido, visto a carência de estudos quanto à geração de RSU e aplicação de indicadores de sustentabilidade na geração desses materiais em pequenos municípios brasileiros, o presente trabalho objetivou avaliar a aplicação da Pegada Ecológica (PE) para os RSU gerados no município de Poço das Trincheiras, Alagoas. Os dados relativos à geração e gravimetria foram obtidos através do Plano Estadual de Resíduos Sólidos do Estado de Alagoas. Foram quantificados a PE per capita e total, bem como a biocapacidade e saldo ecológico da área de estudo. Observou-se uma taxa de geração de RSU per capita em Poço das Trincheiras da ordem de 0,57 quilogramas/dia, totalizando uma produção anual de 496,4 toneladas, com predominância de material orgânico. A PE per capita dos RSU ficou em 0,1159 gha, já a PE total foi de 276,84 gha. A biocapacidade local foi estimada em 26208 gha, permitindo a obtenção de um superávit ecológico de 25931,16 gha. A Pegada Ecológica mostrou-se uma ferramenta importante para se compreender o grau de impacto da geração de RSU.

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Emergy-based ecological footprint analysis for a mega-city: The dynamic changes of Shanghai

Cited by 71 publications

References 71 publications

Assessing the Ecological Carrying Capacity Based on Revised Three-Dimensional Ecological Footprint Model in Inner Mongolia, China

Assessing the Ecological Carrying Capacity Based on Revised Three-Dimensional Ecological Footprint Model in Inner Mongolia, China

Conceptualization and Development of a DFuzzy Model for Low-Carbon Ecocities

Geração de resíduos sólidos urbanos no semiárido brasileiro: análise de sustentabilidade a partir da aplicação da pegada ecológica

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