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Special Issue "Sustainable Ecosystems and Society in the Context of Big and New Data"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 May 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Yichun Xie

Department of Geography and Geology and Institute for Geospatial Research and Education, Eastern Michigan University, Ypsilanti, MI 48197, USA
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Fax: +1-(734) 487-5394
Guest Editor
Prof. Dr. Xinyue Ye

Department of Geography, School of Digital Sciences, and Computational Social Science Lab, Kent State University, Kent, OH 44242, USA
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Interests: GIS; spatial analysis; urban and regional modeling
Guest Editor
Prof. Dr. Clio Andris

Department of Geography and Friendly Cities Lab, The Pennsylvania State University, University Park, PA 16827, USA
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Special Issue Information

Dear Colleagues,

Ecosystem Development and Planning (EDP) faces many challenges as respective environmental, urban, and regional contexts are experiencing rapid change. These changing contexts demand innovative spatial thinking that can capture patterns and processes, and provide spatial strategies for sustainable development. Meanwhile, the volume of data created by an ever-increasing number of geospatial sensor platforms, such as remote sensing and social sensing (including citizen sensors) to collect data at ever increasing spatial, spectral, temporal, and radiometric resolutions, currently exceeds petabytes of data per year and is only expected to increase.

Recent developments in information technology commonly referred to as 'big data' along with the related fields of data science and analytics are needed to process, analyze and realize the value of the overwhelming amount of geospatial sensing data. The research agenda is being substantially transformed and redefined in light of new data and big data, which have transformed the focus of suitability science towards dynamic, spatial and temporal interdependence of human-environment issues. EDP is shifting towards analyzing ever-increasing amounts of large-scale, diverse data in an interdisciplinary, collaborative and timely manner. Rigorous space-time analysis and modeling of ecosystems opens up a rich empirical context for scientific research and policy interventions.

This Special Issue plans to focus on the development of theories, methods and practices on this collaborative and interdisciplinary frontier for this new reality. Building on a series of successful annual conferences in USA and China (http://www.emich.edu/GSES2016), this special issue will bring together leading scholars in related disciplines to share their research on challenges and solutions of Sustainable Ecosystems and Society in the Context of Big and New Data. This Special Issue will be open to the submission of manuscripts from outside the conference as well, provided that they fit within the scope of the Special Issue. Submitted manuscripts will need to be full-length papers that have not been previously published in a substantially-similar format. All manuscripts will be subject to a rigorous peer-review.

Papers of theoretical, methodological and applied nature are equally welcome. Appropriate topics include, but are not limited to:

  • Advanced geo-computational modelling and spatial analysis
  • Applications of geo-informatics in sustainable ecosystems and society
  • Creation of new visualization products that increase the understanding of large and diverse forms of information
  • Discovery of patterns in large volumes of geospatial data through analytic techniques, such as data mining and predictive analytics in applications
  • Ecological, environmental and socioeconomic modeling and coupling
  • Smart city and geo-design
  • Spatial data acquisition through RS and GIS and big-data analytics
  • Technological advances in hardware, storage, data management, networking and computing models, such as visualization and cloud computing for geospatial applications

Prof. Dr. Yichun Xie
Prof. Dr. Xinyue Ye
Prof. Dr. Clio Andris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (12 papers)

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Research

Open AccessArticle Evaluating the Scale Effect of Soil Erosion Using Landscape Pattern Metrics and Information Entropy: A Case Study in the Danjiangkou Reservoir Area, China
Sustainability 2017, 9(7), 1243; doi:10.3390/su9071243
Received: 31 May 2017 / Revised: 12 July 2017 / Accepted: 13 July 2017 / Published: 16 July 2017
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Abstract
The regular patterns of soil erosion tend to change at different scales of observation, affecting the mechanism of soil erosion and its evolution characteristics. This phenomenon has essential scientific significance for the rational allocation of land resources and for studies on sustainable ecosystems.
[...] Read more.
The regular patterns of soil erosion tend to change at different scales of observation, affecting the mechanism of soil erosion and its evolution characteristics. This phenomenon has essential scientific significance for the rational allocation of land resources and for studies on sustainable ecosystems. As an important agricultural area in China, Danjiangkou reservoir is threatened by severe soil erosion. In this study, we selected four kinds of landscape pattern metrics, including patch density, fractal dimension, Shannon diversity index, and connectivity, to analyze soil erosion intensity in the Danjiangkou reservoir area at different scales based on landscape ecological principles. In addition, we determine the optimum research scale of the experimental area by calculating the information entropy value of soil patches at different scales. The findings suggest that: (1) the landscape pattern of soil erosion in the experimental area is obviously scale-dependent, and the responses to scale differ from index to index; (2) as the scale of observation increases, the fragmentation of soil patches is weakened, the stability of different landscape components is enhanced, and the soil becomes less vulnerable to erosion; and (3) based on information entropy theory, 60 m is confirmed to be the optimum scale of this study. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle Establishment of the Sustainable Ecosystem for the Regional Shipping Industry Based on System Dynamics
Sustainability 2017, 9(5), 742; doi:10.3390/su9050742
Received: 17 December 2016 / Revised: 26 April 2017 / Accepted: 27 April 2017 / Published: 4 May 2017
Cited by 1 | PDF Full-text (3797 KB) | HTML Full-text | XML Full-text
Abstract
The rapid development of the shipping industry has brought great economic benefits but at a great environmental cost; exhaust emissions originating from ships are increasing, causing serious atmospheric pollution. Hence, the mitigation of ship exhaust emissions and the establishment of the sustainable ecosystem
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The rapid development of the shipping industry has brought great economic benefits but at a great environmental cost; exhaust emissions originating from ships are increasing, causing serious atmospheric pollution. Hence, the mitigation of ship exhaust emissions and the establishment of the sustainable ecosystem have become urgent tasks, which will require complicated and comprehensive systematic approaches to solve. We address this problem by establishing a System Dynamics (SD) model to help mitigate regional ship exhaust emissions without restricting economic growth and promote the development of the sustainable ecosystem. Factors correlated with ship exhaust emissions are identified, and a causal loop diagram is drawn to describe the complicated interrelations among the correlated factors. Then, a stock-and-flow diagram is designed and variable equations and parameter values are determined to quantitatively describe the dynamic relations among different elements. After verifying the effectiveness of the model, different scenarios for the sustainable development in the study area were set by changing the values of the controlling variables. The variation trends of the exhaust emissions and economic benefits for Qingdao port under different scenarios were predicted for the years 2015–2025. By comparing the simulation results, the effects of different sustainable development measures were analyzed, providing a reference for the promotion of the harmonious development of the regional environment and economy. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle A Geographic Information System (GIS)-Based Analysis of Social Capital Data: Landscape Factors That Correlate with Trust
Sustainability 2017, 9(3), 365; doi:10.3390/su9030365
Received: 2 November 2016 / Revised: 24 February 2017 / Accepted: 24 February 2017 / Published: 2 March 2017
Cited by 1 | PDF Full-text (4968 KB) | HTML Full-text | XML Full-text
Abstract
The field of community sociology has yielded rich insights on how neighborhoods and individuals foster social capital and reap the benefits of interpersonal relationships and institutions alike. Traditionally, institutions and cultural factors have been lauded as catalysts of community social life and cohesion.
[...] Read more.
The field of community sociology has yielded rich insights on how neighborhoods and individuals foster social capital and reap the benefits of interpersonal relationships and institutions alike. Traditionally, institutions and cultural factors have been lauded as catalysts of community social life and cohesion. Yet, the built environment and configuration of the landscape, including infrastructure, amenities and population density, may also contribute to community social capital. In this article, we embedded zip code-level responses from Harvard University’s Saguaro Seminar’s 2006 Social Capital Community Benchmark Survey with a geographic information system. Specifically, we correlated responses on residents’ general trust, trust of one’s neighbors, and trust of members of other racial groups with local urban environmental factors and infrastructural indicators such as housing and street conditions, land use, city form, amenity access (e.g., libraries and schools), home vacancy rates, and home value. We conducted these tests at the national level and for Rochester, NY, due to its many survey responses. We found that housing vacancies drive down levels of social trust, as captured by homeownership rates and tenure, yielding higher levels of social trust, and that certain urban facilities correlate with high trust among neighbors. Results can inform urban planners on the amenities that support sustainable community ties. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle Monitoring Environmental Quality by Sniffing Social Media
Sustainability 2017, 9(2), 85; doi:10.3390/su9020085
Received: 9 November 2016 / Revised: 22 December 2016 / Accepted: 5 January 2017 / Published: 10 February 2017
Cited by 1 | PDF Full-text (4130 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, the environmental pollution and degradation in China has become a serious problem with the rapid development of Chinese heavy industry and increased energy generation. With sustainable development being the key to solving these problems, it is necessary to develop proper techniques for
[...] Read more.
Nowadays, the environmental pollution and degradation in China has become a serious problem with the rapid development of Chinese heavy industry and increased energy generation. With sustainable development being the key to solving these problems, it is necessary to develop proper techniques for monitoring environmental quality. Compared to traditional environment monitoring methods utilizing expensive and complex instruments, we recognized that social media analysis is an efficient and feasible alternative to achieve this goal with the phenomenon that a growing number of people post their comments and feelings about their living environment on social media, such as blogs and personal websites. In this paper, we self-defined a term called the Environmental Quality Index (EQI) to measure and represent people’s overall attitude and sentiment towards an area’s environmental quality at a specific time; it includes not only metrics for water and food quality but also people’s feelings about air pollution. In the experiment, a high sentiment analysis and classification precision of 85.67% was obtained utilizing the support vector machine algorithm, and we calculated and analyzed the EQI for 27 provinces in China using the text data related to the environment from the Chinese Sina micro-blog and Baidu Tieba collected from January 2015 to June 2016. By comparing our results to with the data from the Chinese Academy of Sciences (CAS), we showed that the environment evaluation model we constructed and the method we proposed are feasible and effective. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle A New Optimization Model for the Sustainable Development: Quadratic Knapsack Problem with Conflict Graphs
Sustainability 2017, 9(2), 236; doi:10.3390/su9020236
Received: 12 December 2016 / Revised: 24 January 2017 / Accepted: 4 February 2017 / Published: 9 February 2017
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Abstract
New information technology constantly improves the efficiency of social networks. Using optimization and decision models in the context of large data sets attracts extensive attention. This paper investigates a novel mathematical model for designing and optimizing environmental economic policies in a protection zone.
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New information technology constantly improves the efficiency of social networks. Using optimization and decision models in the context of large data sets attracts extensive attention. This paper investigates a novel mathematical model for designing and optimizing environmental economic policies in a protection zone. The proposed model is referred to as the quadratic knapsack problem with conflict graphs, which is a new variant of the knapsack problem family. Due to the investigated problem processing a high complex structure, in order to solve efficiently the problem, we develop a metaheuristic which is based on the large neighborhood search. The proposed method embeds a construction procedure into a sophistical neighborhood search. For more details, the construction procedure takes charge of finding a starting solution while the investigated neighborhood search is used to generate and explore the solution space issuing from the provided starting solution. In order to highlight our theoretical model, we evaluate the model on a set of complex benchmark data sets. The obtained results demonstrate that the investigated algorithm is competitive and efficient compared to legacy algorithms. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
Open AccessArticle Does Suburbanization Cause Ecological Deterioration? An Empirical Analysis of Shanghai, China
Sustainability 2017, 9(1), 124; doi:10.3390/su9010124
Received: 2 November 2016 / Revised: 11 January 2017 / Accepted: 11 January 2017 / Published: 16 January 2017
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Abstract
Suburbanization in the US largely occurred to solve various urban problems; however, it has also caused many issues, such as the decline of central urban areas, the waste of land resources, and the deterioration of ecological environments in the suburbs. Therefore, the study
[...] Read more.
Suburbanization in the US largely occurred to solve various urban problems; however, it has also caused many issues, such as the decline of central urban areas, the waste of land resources, and the deterioration of ecological environments in the suburbs. Therefore, the study of suburbanization has received considerable attention in academia. Scholars have argued that suburbanization leads to ecological deterioration. To examine this viewpoint, the authors analyzed spatial-temporal changes in the ambient environment, the soil environment, the water environment, and other ecological environments, as well as carbon emissions of the central urban areas and the suburbs, in the suburbanization process exemplified by Shanghai. The results showed that suburbanization indeed caused many changes in ecological and environmental quality, but that the overall environmental quality in the suburbs of Shanghai remained better than that in the central urban area. It is important not to exaggerate the negative impact of suburbanization in metropolitan areas on the quality of the surrounding ecological environments. However, great attention must be given to controlling the diffusion of pollutants resulting from industrial and population suburbanization. It is also crucial to continue strengthening ecological environmental remediation, improvement, and recovery in the central urban area, and to comprehensively promote the coordinated development of agricultural modernization, industrial aggregation, low-carbon urbanization, and ecological sustainability, in both urban and rural areas. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI
Sustainability 2016, 8(12), 1327; doi:10.3390/su8121327
Received: 19 October 2016 / Revised: 27 November 2016 / Accepted: 13 December 2016 / Published: 16 December 2016
Cited by 2 | PDF Full-text (5407 KB) | HTML Full-text | XML Full-text
Abstract
This study proposes a modified vegetation-dependent temperature-vegetation dryness index (TVDI) model for analyzing regional drought disasters in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) of China. First, MODIS monthly normalized difference vegetation index (NDVI), land surface temperature (LST) data and land use/cover data (Land cover
[...] Read more.
This study proposes a modified vegetation-dependent temperature-vegetation dryness index (TVDI) model for analyzing regional drought disasters in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) of China. First, MODIS monthly normalized difference vegetation index (NDVI), land surface temperature (LST) data and land use/cover data (Land cover type2) were pre-processed as a consistent big dataset. The land use/cover data were modified and integrated into six primary types. Then, these land types were used as the base data layer to calculate the TVDI by parameterizing the relationship between the MODIS NDVI and LST data. By emphasizing different types of land uses, this study was able to compare and analyze the differences of the TVDI indices between the entire study area (no consideration of the land types) and the six classified land uses. The soil moisture data were used to validate the modified TVDI values based on different land uses, which confirmed that the modified model more effectively reflected drought conditions. Finally, the aforementioned model was used to analyze the temporal and spatial variation of drought experienced by vegetation cover from 2000 to 2014. The results of the modified model were validated with the synchronized soil moisture and precipitation data. The case study clearly demonstrated that the modified TVDI model, which is based on different vegetation indexes, could better reflect the drought conditions of the study area. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model
Sustainability 2016, 8(12), 1308; doi:10.3390/su8121308
Received: 18 September 2016 / Revised: 15 November 2016 / Accepted: 2 December 2016 / Published: 13 December 2016
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Abstract
Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model
[...] Read more.
Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS), a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008). Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is necessary to take the risk level, physical exposure, and growth stages of crops into consideration together for frost disaster risk prevention planning. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle Evaluating the Sustainability of Nature Reserves Using an Ecological Footprint Method: A Case Study in China
Sustainability 2016, 8(12), 1272; doi:10.3390/su8121272
Received: 30 August 2016 / Revised: 29 October 2016 / Accepted: 1 December 2016 / Published: 6 December 2016
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Abstract
Nature reserves are established to protect ecosystems and rare flora and fauna. However, with the rapid development of the social economy, many nature reserves are facing enormous pressures from human activities. The assessment of the sustainability of nature reserves is a fundamental task
[...] Read more.
Nature reserves are established to protect ecosystems and rare flora and fauna. However, with the rapid development of the social economy, many nature reserves are facing enormous pressures from human activities. The assessment of the sustainability of nature reserves is a fundamental task for the planning and management of such areas. In this study, the sustainability of China’s 319 national nature reserves (NRRs) was evaluated based on an ecological footprint (EF) method. The results indicated that the per capita ecological footprints of all national nature reserves increased 85.86% from 2000 to 2010. Meanwhile, the per capita biocapacity (BC) of all national nature reserves increased slightly, with a rate of increase of 1.79%. The ‘traffic light’ method was adopted to identify the sustainability status of those national nature reserves. It was found that currently (2010) 45% of NRRs were in the condition of ecological deficit. In terms of dynamic changes in EF and BC, only 16% of NRRs were sustainable. The 124 national nature reserves that were in the red light state were mainly distributed in Anhui Province, Chongqing City, Hunan, Guizhou, Fujian, Shandong Province, and Inner Mongolia. The percentage of nature reserves at the red light state in these areas were 83.3%, 66.7%, 64.7%, 62.5%, 58.3%, 57.1%, and 56.5%, respectively. The reserves in the red light state should be included in the priority concern level and should be strictly controlled in terms of population growth and the intensity of exploitation. The results of this study will provide more effective data for reference and for decision making support in nature reserve protection. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle The Delimitation of Urban Growth Boundaries Using the CLUE-S Land-Use Change Model: Study on Xinzhuang Town, Changshu City, China
Sustainability 2016, 8(11), 1182; doi:10.3390/su8111182
Received: 16 July 2016 / Revised: 20 October 2016 / Accepted: 12 November 2016 / Published: 16 November 2016
Cited by 2 | PDF Full-text (13045 KB) | HTML Full-text | XML Full-text
Abstract
Over the past decades, urban growth boundaries (UGBs) have been regarded as effective tools applied by planners and local governments to curb urban sprawl and guide urban smart growth. The UGBs help limit urban development to suitable areas and protect surrounding agricultural and
[...] Read more.
Over the past decades, urban growth boundaries (UGBs) have been regarded as effective tools applied by planners and local governments to curb urban sprawl and guide urban smart growth. The UGBs help limit urban development to suitable areas and protect surrounding agricultural and ecological landscapes. At present, China’s Town and Country Planning Act officially requires the delimitation of UGBs in a city master planning outline and in central urban area planning. However, China’s practices in UGBs are usually determined by urban planners and local authorities, and lack a sound analytical basis. Consequently, Chinese UGBs are often proven to be inefficient for controlling urban expansion. In this paper, take the fast-growing Xinzhuang town of Changshu city, eastern China as an example, a new method towards establishing UGBs is proposed based on land-use change model (the Conversion of Land Use and its Effects at Small regional extent, CLUE-S). The results of our study show that the land-use change and urban growth simulation accuracy of CLUE-S model is high. The expansion of construction land and the decrease of paddy field would be the main changing trends of local land use, and a good deal of cultivated land and ecological land would be transformed into construction land in 2009–2027. There is remarkable discordance in the spatial distribution between the simulated UGBs based on the CLUE-S model and the planned UGBs based on the conventional method, where the simulated results may more closely reflect the reality of urban growth laws. Therefore, we believe that our method could be a useful planning tool for the delimitation of UGBs in Chinese cities. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China
Sustainability 2016, 8(11), 1124; doi:10.3390/su8111124
Received: 26 September 2016 / Revised: 28 October 2016 / Accepted: 29 October 2016 / Published: 1 November 2016
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Abstract
During the past 30 year of economic growth, China has also accumulated a huge environmental pollution debt. China’s government attempts to use a variety of means, including tax instruments to control environmental pollution. After nine years of repeated debates, the State Council Legislative
[...] Read more.
During the past 30 year of economic growth, China has also accumulated a huge environmental pollution debt. China’s government attempts to use a variety of means, including tax instruments to control environmental pollution. After nine years of repeated debates, the State Council Legislative Affairs Office released the Environmental Protection Tax Law (Draft) in June 2015. As China’s first environmental tax law, whether this conservative “Environmental Fee to Tax (EFT)” reform could improve the environment has generated controversy. In this paper, we seek insights to this controversial issue using the machine learning approach, a powerful tool for environmental policy assessment. We take Hubei Province, the first pilot area as a case of EFT, and analyze the institutional incentive, behavior transformation and emission intensity reduction performance. Twelve pilot cities located in Hubei Province were selected to estimate the effect of the reform by using synthetic control and a rapid developing machine learning method for policy evaluation. We find that the EFT reform can promote emission intensity reduction. Especially, relative to comparable synthetic cities in the absence of the reform, the average annual emission intensity of Sulfur Dioxide (SO2) in the pilot cities dropped by 0.13 ton/million Yuan with a reduction rate of 10%–32%. Our findings also show that the impact of environmental tax reform varies across cities due to the administrative level and economic development. The results of our study are also supported by enterprise interviews. The EFT improves the overall environmental costs, and encourages enterprises to reduce emissions pollution. These results provide valuable experience and policy implications for the implementation of China’s Environmental Protection Tax Law. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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Open AccessArticle Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier
Sustainability 2016, 8(9), 921; doi:10.3390/su8090921
Received: 16 July 2016 / Revised: 29 August 2016 / Accepted: 5 September 2016 / Published: 9 September 2016
Cited by 1 | PDF Full-text (8556 KB) | HTML Full-text | XML Full-text
Abstract
Online social media crowdsourced photos contain a vast amount of visual information about the physical properties and characteristics of the earth’s surface. Flickr is an important online social media platform for users seeking this information. Each day, users generate crowdsourced geotagged digital imagery
[...] Read more.
Online social media crowdsourced photos contain a vast amount of visual information about the physical properties and characteristics of the earth’s surface. Flickr is an important online social media platform for users seeking this information. Each day, users generate crowdsourced geotagged digital imagery containing an immense amount of information. In this paper, geotagged Flickr images are used for automatic extraction of low-level land use/land cover (LULC) features. The proposed method uses a naive Bayes classifier with color, shape, and color index descriptors. The classified images are mapped using a majority filtering approach. The classifier performance in overall accuracy, kappa coefficient, precision, recall, and f-measure was 87.94%, 82.89%, 88.20%, 87.90%, and 88%, respectively. Labeled-crowdsourced images were filtered into a spatial tile of a 30 m × 30 m resolution using the majority voting method to reduce geolocation uncertainty from the crowdsourced data. These tile datasets were used as training and validation samples to classify Landsat TM5 images. The supervised maximum likelihood method was used for the LULC classification. The results show that the geotagged Flickr images can classify LULC types with reasonable accuracy and that the proposed approach improves LULC classification efficiency if a sufficient spatial distribution of crowdsourced data exists. Full article
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
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