Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL)

Author(s):  
Christina Corbane ◽  
Panagiotis Politis ◽  
Martino Pesaresi ◽  
Thomas Kemper ◽  
Alice Siragusa
2019 ◽  
Vol 8 (2) ◽  
pp. 96 ◽  
Author(s):  
Michele Melchiorri ◽  
Martino Pesaresi ◽  
Aneta Florczyk ◽  
Christina Corbane ◽  
Thomas Kemper

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.


Author(s):  
Michele Melchiorri ◽  
Martino Pesaresi ◽  
Aneta J. Florczyk ◽  
Christina Corbane ◽  
Thomas Kemper

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet based mainly on two quantitative factors: i) the spatial distribution (density) of built-up structures and ii) the spatial distribution (density) of resident people. Both factors are observed in the long-term temporal domain and per uniform surface units in order to support trends and indicators for monitoring the implementation of international framework agreements. The GHSL uses various input data including global, multi-temporal archives of fine-scale satellite imagery, census data, and volunteered geographic information. In this paper, we present the characteristics of GHSL information to demonstrate how original frameworks of data and tools rooted on Earth Observation could support Sustainable Development Goals monitoring. In particular, we demonstrate the reach of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for the Sustainable Development Goal 11. Our experiments produce a global estimate for the Land Use Efficiency indicator (SDG 11.3.1) for 10,000 urban centers, calculating the ratio of land consumption to population growth rate that took place between 1990 and 2015. The results of our research demonstrate that there is a potential to lift SDG 11.3.1 from a tier 2 as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.


Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 715
Author(s):  
Yingkai Tang ◽  
Kun Wang ◽  
Xuanming Ji ◽  
He Xu ◽  
Yangqing Xiao

Rapid urbanization has provided a strong impetus for the economic growth of China, but it has also caused many problems such as inefficient urban land use and environmental pollution. With the popularization of the concept of green and sustainable development, the Environmental-Social-Governance (ESG) assessment concept is widely accepted. The government and residents are paying more and more attention to environmental issues in urban development, and environmental protection has formed an important part of urban development. In this context, this study takes 26 cities in the Yangtze River Delta as examples to build an evaluation system for urban land-use efficiency under green development orientation. The evaluation system takes into account the inputs of land, capital, labor, and energy factors in the process of urban development. Based on emphasizing economic output, the social benefits and undesired outputs brought about by urban development are taken into account. This paper measures urban land use efficiency by the super-efficiency SBM model, and on this basis, analyses the spatial-temporal evolution characteristics of urban land-use efficiency. Further, this paper measures urban land use efficiency without considering undesired outputs and compares the two evaluation methods. Again, the comparison illustrates the rationality of urban land use efficiency evaluation system under green development orientation.


Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 303
Author(s):  
Xinhai Lu ◽  
Yifeng Tang ◽  
Shangan Ke

The construction and operation of high-speed rail (HSR) has become an important policy for China to achieve efficiency and fairness and promote high-quality economic growth. HSR promotes the flow of production factors such as labor and capital and affects economic growth, and may further affect urban land use efficiency (ULUE). To explore the impact of HSR on ULUE, this paper uses panel data of 284 cities in China from 2005 to 2018, and constructs Propensity Score Matching-Differences in Differences model to evaluate the effect of HSR on ULUE. The result of entire China demonstrates that the HSR could significantly improves the ULUE. Meanwhile, this paper also considers the heterogeneity of results caused by geographic location, urban levels and scales. It demonstrates that the HSR has a significantly positive effect on ULUE of Eastern, Central China, and large-sized cities. However, in Western China, in medium-sized, and small-sized cities, the impact of HSR on ULUE is not significant. This paper concludes that construction and operation of HSR should be linked to urban development planning and land use planning. Meanwhile, the cities with different geographical locations and scales should take advantage of HSR to improve ULUE and promote urban coordinated development.


Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 657
Author(s):  
Aiping Wang ◽  
Weifen Lin ◽  
Bei Liu ◽  
Hui Wang ◽  
Hong Xu

Frontier research primarily focuses on the effect of urban development models on land use efficiency, while ignoring the effect of new-type urban development on the green land use efficiency. Accordingly, this paper employs a super efficiency slacks-based measure (super-SBM) model with undesirable outputs to measure the green land use efficiency based on panel data from 152 prefecture-level cities for the period 2004–2017. We construct a difference-in-differences (DID) model in this paper to test the impact of smart city construction on the green utilization efficiency of urban land and its transmission mechanism. The results showed that: (1) The smart city construction significantly improved the green utilization efficiency of urban land, increasing the general efficiency by 15%. (2) There is significant city-size heterogeneity in the effect of smart city construction on improving green utilization efficiency of urban land. The policy effect is more obvious in mega cities and above than in very-large-sized cities. (3) The city-feature heterogeneity results reveal that, in cities with a higher level of human capital, financial development, and information infrastructure, the effectiveness of smart city construction in improving the green utilization efficiency of urban land are more obvious, and in cities with a higher level of financial development, the effects of the urban policy were more optimal. (4) The smart city construction promotes the green utilization efficiency of urban land through by the information industry development and the regional innovation capabilities.


Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Chen ◽  
Rui He ◽  
Qun Wu

With the rapid and unbalanced development of industry, a large amount of cultivated land is converted into industrial land with lower efficiency. The existing research is extensively concerned with industrial land use and industrial development in isolation, but little attention has been paid to the relationship between them. To help address this gap, the paper creates a new efficiency measure method for industrial land use combining Subvector Data Envelope Analysis (DEA) with spatial analysis approach. The proposed model has been verified by using the industrial land use data of 30 Chinese provinces from 2001 to 2013. The spatial autocorrelation relationship between industrial development and industrial land use efficiency is explored. Furthermore, this paper examines the effects of industrial development on industrial land use efficiency by spatial panel data model. The results indicate that the industrial land use efficiency and the industrial development level in the provinces of eastern region are higher than those of the western region. The spatial distribution of industrial land use efficiency shows remarkable positive spatial autocorrelation. However, the level of industrial development has obvious negative spatial autocorrelation since 2009. The improvement of industrial development has a significant positive impact on the industrial land use efficiency.


2015 ◽  
Vol 21 (5) ◽  
pp. 747-758 ◽  
Author(s):  
Hannah H. E. van Zanten ◽  
Herman Mollenhorst ◽  
Cindy W. Klootwijk ◽  
Corina E. van Middelaar ◽  
Imke J. M. de Boer

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