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In today's competitive world, gathering precise context knowledge is a critical barrier for industry practitioners, professionals, and researchers in supply chain management, impeding their capacity to acquire expertise and give effective management awareness. In this regard, this study aimed to identify the data-driven capabilities of supply chain management. Realising the abovementioned issues, the existing literature on supply chain management needs to be further reviewed to explore the data-driven capabilities for sustainable supply chains. The study presented a systematic literature review on a data-driven sustainable supply chain to achieve this goal. One hundred ninety-seven documents from various publications were identified for bibliometric and network analysis from the Scopus database using specific keywords related to the research concept published during 2013–2023 through inclusion and exclusion criteria. The emerging themes were identified using the "R" package and reviewed the papers were reviewed in theme directions. The study showed that implementing data-driven and sustainable technologies could assist strategic decision-making. Also, Data-driven solutions can effectively improve sustainability in supply chain network operations performance. The study demonstrates that big data can contribute to achieving sustainability goals through bibliometric analysis and critique of previous papers published in the specialised field. [ABSTRACT FROM AUTHOR]

Carbon markets are widely recognized as effective strategies for regulating carbon emissions from the enterprises of the high-carbon industry. Accurate monitoring of emissions in these enterprises is crucial for ensuring fair and trusted carbon trading. However, traditional monitoring methods have encountered various challenges including poor accuracy, low frequency, significant hysteresis and weak credibility. Addressing these concerns, this paper introduces a novel, multi-trusted, high-frequency monitoring approach for the measurement of both direct and indirect carbon emissions of enterprises, utilizing externally available multi-source big data, vertical federated long short-term memory network with a self-attention mechanism (VF-LSTMSA) and additive homomorphic encryption. The efficacy of this model is thoroughly assessed via case studies on six hourly datasets from the power and steel sectors. Our principal findings include: (1) In the current scenario, the proposed VF-LSTMSA model demonstrates superior federated efficiency, improving with the inclusion of more federated participants. (2) The VF-LSTMSA model surpasses other prevalent AI models, achieving a Mean Absolute Percentage Error (MAPE) of less than 9% across power and steel datasets. (3) The advanced high-frequency carbon emission monitoring method exhibits errors of +0.28%, +1.29%, and −0.41% in the current scenario monitoring task, suggesting that the model effectively leverages multi-source externally available big data to precisely monitor the target enterprise's carbon emissions while safeguarding multi-party data security. These findings suggest that the proposed monitoring method can accurately track corporate hourly carbon emissions, significantly improving the carbon trading and verification processes for enterprises [ABSTRACT FROM AUTHOR]

Abstract How to make an effective safety decision is always a topic of intense interest in the safety management field. Safety-Related Data (SRD) are the most valuable assets for organizations' Safety Decision-Making (SDM), especially in the era of big data. This paper focuses on the potentially important value of SRD in SDM, and aims to systematically answer some fundamental questions concerning a new paradigm for SDM, known as data-driven SDM, from a theoretical perspective. These questions examine (1) what it is, (2) what its benefits are, (3) what its theoretical foundations are, (4) what its fundamental elements consist of, (5) what factors influencing it are, and (6) how the organization should implement it and realize smart safety management by using it. Other theoretical and practical contributions include a discussion of the problems of traditional SDM approaches and how to solve them, a rationale for creating and studying data-driven SDM, and suggestions for future research. This paper is the first to study the basic questions of data-driven SDM specifically, thus its results hold important implications for future research and practice on data-driven SDM and smart safety management. Highlights • The definition and benefits of data-driven SDM were proposed. • The theoretical foundations, and fundamental elements of data-driven SDM were discussed. • The factors that influence data-driven SDM were analysed. • The directions for future research on data-driven SDM were discussed. [ABSTRACT FROM AUTHOR]

Big data industry development and carbon dioxide emissions reduction are the crucial driver and objective functions of economic growth, respectively. Whether the two types of corresponding policies can achieve the "win-win" effect is the key to green development transformation in the future. However, little attention has been paid to the influence of big data industry development on carbon dioxide emissions. Based on the quasi-natural experiment in China's national big data comprehensive experimental area, the paper identifies the causal effect between big data industry development and carbon dioxide emissions. Specifically, the panel data of 282 cities in China spanning from 2010 to 2019 are used in regression analysis. The study reveals that the big data industry development has a substantial impact in reducing carbon dioxide emissions. Meanwhile, the findings are confirmed by a set of rigorous robustness checks. The heterogeneity analyses suggest that the effect of inhibiting carbon dioxide emissions due to the big data industry development is more significant in eastern & central China, and large-sized cities. In addition, the big data industry reduces carbon dioxide emissions through the effects of industrial structure optimization, technological innovation, and resource allocation. Our findings contribute to the nexus between big data industry development and carbon dioxide emissions. In light of these findings, the paper recommends that governments integrate economic growth and environmental protection policies by promoting big data industry development. • The paper provides a novel insight into the CO 2 emission from the perspective of big data industry. • Based on quasi-natural experiment, the paper has identified the causal effect between the big data industry development and CO 2 emissions with the Difference-in-Difference (DID) methodology. • Big data industry development effectively stimulate the reduction of CO 2 emissions. [ABSTRACT FROM AUTHOR]

The present paper measures external effects in a specific type of common-pool resource, an information commons. Employing a novel dataset related to more than 700,000 transactions in distinct libraries during a 10-year period (2006–2015), we estimate the external effects of actions of library users who were subject to a non-monetary sanction (professors and university employees) on users who were subject to a monetary sanction (students). Additionally, we estimate peer effects among users, considering the number of items they borrow from the library. When investigating externalities, we uncover a “crowding-out” effect: for an additional unity in professors and employees' counts, there is an approximate one-to-one decrease in students' counts. In the case of peer effects, we find a positive influence of group behavior on individuals: for every 100 books borrowed by a user's peer group, there is a rise of three books per user, on average. The results reported in this paper have important implications for theories based on common-pool resource management, as well as public goods provision. [ABSTRACT FROM AUTHOR]

Due to increasingly customised manufacturing, unpredictable ambient working conditions in shop floors and stricter requirements on sustainability, it is challenging to achieve energy efficient optimisation for machining processes. This paper presents a novel Cyber Physical System (CPS) and Big Data enabled machining optimisation system to address the above challenge. The innovations and characteristics of the system include the following four aspects: (1) a novel process of “scheduling, monitoring/learning, rescheduling” is designed to enhance system adaptability during manufacturing lifecycles; (2) an innovative energy model to support energy efficient optimisation over manufacturing lifecycles is developed. The energy model, which is enabled by CPS, Big Data analytics and intelligent learning algorithms, considers dynamic and aging conditions of machine tool systems during manufacturing lifecycles; (3) an effective evolutional algorithm based on Fruit Fly Optimisation (FFO), is applied to generate an adaptive energy efficient schedule, and improve schedule when there are significantly varying working conditions and adjustments on the schedule are necessary (that is rescheduling); (4) the system has been successfully deployed into European machining companies to verify capabilities. According to the results, around 40% energy saving and 30% productivity improvement have been achieved in the companies. A practical case study presented in this paper demonstrates the effectiveness and great potential of applicability of the system in practice. [ABSTRACT FROM AUTHOR]

Chinese urbanization has generated great impacts on the world since the reform and opening up. However, urban problems, e.g., environmental pollution, resources shortage, and traffic jam, have been more and more serious for urban management and development. Smart city has been put forward as an effective approach to achieve better urban management recently. Smart city aims to realize the integration of municipal service, business, transportation, water, energy source and other urban sub-systems through close combination of human wisdom and information communication techniques (ICTs). As a result, the link and synergy of information could be ultimately established with ICTs, e.g., internet, internet of things, cloud computing. Yet, few studies have been conducted to systematically link smart city with big data in China. This paper aims to put forward a development framework of smart city with Chinese characteristics against the background of big data. Key actions, including rational planning of city infrastructures, the establishment and improvement of long-acting mechanism, the effective performance of city managerial function, are proposed to realize the development idea. Meanwhile, this paper also investigates the risks embedded in development of smart city with Chinese characteristics, e.g., information safety, weak emergency responding capacity and poor independent research and development capacity of core technology. This study can facilitate Chinese local governments to systematically plan smart city before clinging the hot concept in a rush. [ABSTRACT FROM AUTHOR]

Although a large number of reviews have studied different scales of forest ecosystem services, few have concentrated on accounting the spatiotemporal dynamic changes of forest ecosystem services in a region. To make a more accurate accounting, this paper selects intense data sets that assess the physical quantity and value of forest ecosystem services in Anhui provinces from 2009 to 2014. Regulating and supporting services such as water conservation, carbon sequestration and oxygen release, soil conservation and air purification are included in the paper as variables. Optimize analysis was further conducted in order to identify the spatial and temporal heterogeneity during the study period. We establish a dynamic ecosystem services assessment model with the coefficient of spatial heterogeneity and coefficients of scarce resources and social development to analyze the trend of the value in Anhui province. This model is designed for comparisons of forest services in different scales in Anhui province. The results demonstrate that the total value of forest ecosystem services is beyond the previous estimation while using spatiotemporal visualization. The implications are that the evaluation of forest ecosystem services should be measured more accurately with the help of big data in the future. [ABSTRACT FROM AUTHOR]

The development of China's forestry resources has never been more challenging due to serious problems such as shortage, inferiority and uneven distribution of forestry resources. Therefore, the study and evaluation of China's forestry resources has a great significance to improve efficiency and ensure the sustainable development of the forestry resource. Meanwhile, the vast territory, huge population and widespread forest landscape of China have led to the numerous indexes and the huge data. To perform the research and evaluation accurately, this paper utilized the big data theory to analyze the relevant data of China's forestry resources. This study collected the data from 31 inland provinces and municipalities of China from 2005 to 2013, after which we carefully examined economic, social and ecological factors to choose assessment indexes and processed data accordingly. Firstly, we performed a cross-sectional dataset analysis using the method of data envelopment analysis to investigate the forestry resources efficiency in 31 inland provinces and municipalities of China in years 2008, 2012 and 2013. Secondly, we analyzed time series data of the 31 inland provinces and municipalities from 2005 to 2013 using the Malmquist total factor productivity index method. Our results showed the dominant factor that restraining forestry resources efficiency for the 31 inland provinces and municipalities is the implemented technology. So we suggest increasing the investment in science and technology to improve the overall efficiency of forestry resources, along with improvement of operation and management by relevant administrative departments to improve technology utilization. The innovation of this paper lies in the dynamic process of analysis. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose and test a theoretical framework to explain resilience in supply chain networks for sustainability using unstructured Big Data, based upon 36,422 items gathered in the form of tweets, news, Facebook, WordPress, Instagram, Google+, and YouTube, and structured data, via responses from 205 managers involved in disaster relief activities in the aftermath of Nepal earthquake in 2015. The paper uses Big Data analysis, followed by a survey which was analyzed using content analysis and confirmatory factor analysis (CFA). The results of the analysis suggest that swift trust, information sharing and public–private partnership are critical enablers of resilience in supply chain networks. The current study used cross-sectional data. However the hypotheses of the study can be tested using longitudinal data to attempt to establish causality. The article advances the literature on resilience in disaster supply chain networks for sustainability in that (i) it suggests the use of Big Data analysis to propose and test particular frameworks in the context of resilient supply chains that enable sustainability; (ii) it argues that swift trust, public private partnerships, and quality information sharing link to resilience in supply chain networks; and (iii) it uses the context of Nepal, at the moment of the disaster relief activities to provide contemporaneous perceptions of the phenomenon as it takes place. [ABSTRACT FROM AUTHOR]

Air pollutions from transportation sector have become a serious urban environmental problem, especially in developing countries with expending urbanization. Cleaner technologies advancement and optimal regulation on the transporting behaviors and related design in infrastructures is critical to address above issue. To understand the spatial and temporal emissions pattern within transportation lays the foundation for design on better infrastructures and guidance on low-carbon transportation behaviors. The feasibility of Global Positioning System (GPS) and emerging big data analysis technique enable the in-depth analysis on this topic, while to date, applications had been rather few. With this circumstance, this paper analyzed the taxi's energy consumption and emissions and their spatial-temporal distribution in Shanghai, one of the most famous mega cities in China, applying big data analysis on GPS data of taxies. Spatial and temporal features of energy consumptions and pollutants emissions were further mapped with geographical information system (GIS). Results highlighted that, spatially, the energy consumption and emission presented a distribution of dual-core cyclic structure, in which, two hubs were identified. One was the city center, the other was Hongqiao transport hub, the activities and emission was more concentrated in the west par of Huangpu River. Temporally, the highest activity and emission moment was 9–10AM, the second peak occurred in 7–8PM, which were both the traffic rush period. The lowest activity/emission moment was 3–4AM. Causal mechanism for such distribution was further investigated, so as to improve the driving behaviors. Through the exploration of spatial and temporal emissions distribution of taxis via big dada technique, this paper provided enlightening insights to policy makers for better understanding on the travel patterns and related environmental implications in Shanghai metropolis, so as to support better planning on infrastructures system, demand side management and the promotion on low-carbon life styles. [ABSTRACT FROM AUTHOR]

Constructing an eco-civilization is crucial in achieving green, low-carbon development, and thus bridging the gap between theory and practice is imperative to better promote urban ecological transformation. At present, there are inconsistencies and imbalances between theoretical research and practical efforts to achieve an eco-civilization. This paper uses bibliometric analysis and Latent Dirichlet Allocation to analyze China's progress towards an eco-civilization at the theoretical and practical levels. The 'theory' is analyzed using 632 articles, while the 'practice' is analyzed using 100 eco-civilization pilot zone reports. The two are compared, with results showing: from 2015 onwards, the theory is moving in an interdisciplinary direction, with seven themes focusing on macro topics. eco-civilization construction projects have multiple overlapping themes, and the growing connection between energy consumption, economic growth and environmental impacts driven by significant projects is the key to improving the level of regional ecological development. the theory and practice are similar in that both are concerned with environmental protection, ecological education, and ecotourism. However, eco-civilization construction projects lack forward-looking and dynamic development tracking of low-carbon research. the paper proposes that dynamic evaluation and project tracking methods should be applied to monitor critical indicators to achieve a solid link between the theory and practice during eco-construction projects. This paper also proposes academia should do more research on townships and other micro level phenomena to promote climate change and energy revolution in the construction of a Chinese eco-civilization. • Analysis of the similarities and differences between theoretical research and practical measures of eco-civilization. • Academic research focuses on sustainability, while practical activities lack attention to climate change. • Optimizing the energy-economy-environment integration project is the key to improving China's ecology. • Environmental protection, eco-civilization education, and eco-tourism are common concerns in the theory and practice. • Suggestions include focusing on crucial indicators and micro-level activities. [ABSTRACT FROM AUTHOR]

This paper aims to study the determinants that affect sustainability performance in manufacturing companies and the impact of business intelligence and big data analytics capability on performance considering the moderating role of green knowledge management. An online questionnaire survey was conducted with 488 ISO14001-certified manufacturing companies in Malaysia to test the framework developed in this study. Nonprobability sampling was used in this study. A total of 283 responses were received, whereby the data was then analyzed using structural equation modeling via SmartPLS 4 software. The results indicated that the significant factor determining big data analytics capability is business intelligence, and the positive outcome of big data analytics capability is sustainability performance. Big data analytics capability also plays a mediating role in the positive relationship between business intelligence and sustainability performance, whereby green knowledge management does not moderate the relationship between big data analytics capability and sustainability performance. Even though business intelligence has a substantial impact on sustainability performance, the relationship is not stronger when the level of green knowledge management changes. This research provides awareness to manufacturing companies on the critical factors contributing to big data analytics capability and its outcomes on corporate sustainability performance. [ABSTRACT FROM AUTHOR]

Abstract Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and sustainable. As one of the most important technologies for smart manufacturing, big data analytics can uncover hidden knowledge and other useful information like relations between lifecycle decisions and process parameters helping industrial leaders to make more-informed business decisions in complex management environments. However, according to the literature, big data analytics and smart manufacturing were individually researched in academia and industry. To provide theoretical foundations for the research community to further develop scientific insights in applying big data analytics to smart manufacturing, it is necessary to summarize the existing research progress and weakness. In this paper, through combining the key technologies of smart manufacturing and the idea of ubiquitous servitization in the whole lifecycle, the term of sustainable smart manufacturing was coined. A comprehensive overview of big data in smart manufacturing was conducted, and a conceptual framework was proposed from the perspective of product lifecycle. The proposed framework allows analyzing potential applications and key advantages, and the discussion of current challenges and future research directions provides valuable insights for academia and industry. Highlights • The implications and purposes of sustainable smart manufacturing were defined. • A comprehensive review of big data in smart manufacturing was conducted. • A framework of big data analytics in sustainable smart manufacturing was proposed. • The potential applications and advantages of the proposed framework were evaluated. • The challenges and directions for further research were identified. [ABSTRACT FROM AUTHOR]

Abstract In these years, more and more manufacturers implement closed-loop operations and incorporate remanufacturing into its manufacturing system, which forms manufacturing-remanufacturing operations. To increase the efficiency of used product collection, many remanufacturers have established quality valuation system to acquire big data of used products. A quality-dependent acquisition process helps remanufacturers collect used products according to products' actual condition and perceived value from the perspective of consumers, but may incur extra operation cost. This study aims to explore the influence of quality valuation big data system on manufacturers' manufacturing/remanufacturing operation decisions. This paper studies a manufacturer which produces new products and remanufactures the returned products, and sells two kinds of products to the same market. We focus on quantity decisions on manufacturers' manufacturing/remanufacturing operations. Focusing on the availability of big data of used products, two optimization models, fixed collection pricing mechanism and discriminatory collection pricing mechanism, are analyzed to explore the impact of quality big data of used products on firms' manufacturing–remanufacturing operations. As the obtained conclusions, in both fixed and discriminatory collection pricing mechanisms, the manufacturer takes full/partial remanufacturing strategy under different conditions. And the manufacturer prefers different collection pricing mechanisms according to various levels of consumers' perceived value of used products and the acquisition cost of big data. [ABSTRACT FROM AUTHOR]

Abstract A new generation of bike-sharing services is emerging in China. With this service, bikes can be unlocked and paid by using a smartphone and then picked up and left anywhere at users' convenience. The unprecedented development of dockless bike-sharing services results in considerable socioeconomic and environmental benefits but also creates new urban issues. One of the most severe issues is users' inappropriate parking behaviour. To solve this problem, electric fence (or geo-fence) policy and technology have been introduced in China to guide users to park bikes in designated zones. In this paper, we first propose a methodological framework to support electric fence planning for dockless bike-sharing services. We then apply our framework in a case study of Shanghai using a big dataset of bike trips. Results show that when the number of planned electric fences is 7,500, our electric fence plan can cover 91.8% of total parking demand. In addition, our plan can ensure that at least 95.8% of all bikes can be docked at one of planned electric fences and can help efficiently and accurately determine suitable locations for setting up planned electric fences. Highlights • We discuss electric fence (geo-fence) policy and technology used to regulate inappropriate parking behaviour. • We propose a methodological framework to support electric fence planning. • We apply our methods in a case study of Shanghai and present the results in six scenarios. • We adopt a big data approach to support our analysis. [ABSTRACT FROM AUTHOR]

Abstract A layout design based on large scale (Big) data is more efficient and effective in today's competitive market. Due to variations in product demands, varying product mix, and addition or deletion of products, layout of industry needs to be robust and sustainable. A robust and sustainable industry layout design is capable to handle the variations and is seen as first step towards Industry 4.0 to keep precise and accurate manufacturing of products in given due time. Poor layout design decreases the precision and accuracy in manufacturing of products and increases the production time. To design layout for Industry 4.0, this work proposes an embedded SA based meta-heuristic and principal component analysis (PCA) approach using large scale data to solve sustainable robust stochastic cellular facility layout problem (sustainable-RSCFLP). The data for the problem is collected considering basic 3Vs of Big Data i.e. Volume, Variety, and Velocity. Fourteen different criteria are identified and evaluated by 100 experts to group them based on their Eigen values using PCA. These are Distance, Adjacency, Shape ratio, Flexibility, Maintenance, Accessibility, Hazardous movement, Noise Level, Aesthetic, Safety, WIP and inventory, Space utilization, Capital equipment utilization, and Robustness. These fourteen criteria are clustered in to four factors using PCA approach. These clusters are defined as Material Handling Distance , Maintenance , Adjacency , and Hazard. The material handling distance comprises of three criteria such as Distance, Capital equipment utilization, and space utilization. In the same way, Maintenance comprises of three criteria such as Maintenance, Work in Process and inventory, and Accessibility. Similarly, Adjacency comprises of four criteria such as Shape ratio, Robustness, Adjacency, and Flexibility. Finally, Hazard comprises of four criteria such as Noise level, Aesthetics, Safety and Hazardous movement. In addition to these four clusters, fifth factor i.e. electrical energy consumption (EEC) is taken separately for each layout alternative to make the proposed layout environmentally sustainable. Thus, the proposed layout considers all pillars of sustainability i.e. EEC (maps environmental sustainability), Maintenance and Hazards (maps social sustainability), and Material Handling (maps economic sustainability) for designing cellular facility layout problem. Further, pool of layouts is generated using embedded SA based meta-heuristic. Each layout is evaluated over each clusters and EEC is calculated for each layout as to make the layout design sustainable. Furthermore layouts are ranked using TOPSIS, IRP, and weighted-IRP qualitative approaches, and a consensus ranking is finally provided to select an efficient sustainable robust layout for Industry 4.0. Highlights • Paper propose a sustainable robust layout design for Industry 4.0 using Big Data. • Fourteen criteria are considered. PCA used to cluster it in to four factors. • EEC is considered as a fifth one to incorporate sustainability in layout. • SA based embedded meta-heuristic is proposed for sustainable robust layout. • Three multi attribute techniques viz. IR, weighted-IRP and TOPSIS are applied. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO 2 -footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development. • Innovative approach to designing sustainable building materials using sequential learning. • Provides clear guidance for design process, can advance sustainable material development immediately. • Challenges data-driven materials dev. practices by prioritizing practical design frameworks over accurate models. • Significant reduction in dev. time, lower research costs, and straightforward predictions through sequential learning. • Large data sets of different types of alkali-activated building materials used for evaluation. [ABSTRACT FROM AUTHOR]

The Big Data driven approach has become a new trend for manufacturing optimisation. In this paper, an innovative Big Data enabled Intelligent Immune System (I 2 S) has been developed to monitor, analyse and optimise machining processes over lifecycles in order to achieve energy efficient manufacturing. There are two major functions in I 2 S: (1) an Artificial Neural Networks (ANNs)-based algorithm and statistical analysis tools are used to identify the abnormal electricity consumption patterns of manufactured components from monitored Big Data. An intelligent immune mechanism is devised to adapt to the condition changes and process dynamics of machining systems; (2) a re-scheduling algorithm is triggered if abnormal manufacturing conditions are detected thereby achieving multi-objective optimisation in terms of energy consumption and manufacturing performance. In this research, Computer Numerical Controlled (CNC) machining processes and industrial case studies have been used for system validation. The novelty of I 2 S is that Big Data analytics and intelligent immune mechanisms have been integrated systematically to achieve condition monitoring, analysis and energy efficient optimisation over manufacturing execution lifecycles. The applicability of the system has been validated by multiple industrial trials in European factories. Around 30% energy saving and over 50% productivity improvement have been achieved by adopting I 2 S in the factories. [ABSTRACT FROM AUTHOR]

The regional energy management body has a large amount of regional industrial companies’ energy consumption data. It can evaluate the energy utilization of listed regional industrial companies based on the total data and, then, find the key points for understanding the resources usage patterns, identifying the problematic companies, and establishing good energy consumption practices. This paper reviews the research progress on big data analysis and industrial energy efficiency evaluation and focuses on the energy efficiency evaluation methods based on energy consumption process analysis and big data mining approach. Based on K-means and multi-dimensional association rules algorithm, to analyze the characteristics of regional energy consumption in different industries and companies, we cluster single industry in K-means and finding their levels of water and energy consumption. This classification provided us a reference point to identify the industries and companies to focus on and locate the bad consumption practices and environmental performance. Then, multi-dimensional association rules are used to find the correlation of processes, companies and energy efficiency to guide the energy conservation in regional energy monitor. The output of our research is a working Big Data analytics platform and the results generated from advance analytics techniques applied specifically to solve regional energy efficiency problems. [ABSTRACT FROM AUTHOR]

Optimization of the process of product lifecycle management is an increasingly important objective for manufacturing enterprises to improve their sustainable competitive advantage. Originally, this approach was developed to integrate the business processes of an organization and more effectively manage and utilize the data generated during lifecycle studies. With emerging technologies, product embedded information devices such as radio frequency identification tags and smart sensors are widely used to improve the efficiency of enterprises' routine management on an operational level. Manufacturing enterprises need a more advanced analysis approach to develop a solution on a strategic level from using such lifecycle Big Data. However, the application of Big Data in lifecycle faces several challenges, such as the lack of reliable data and valuable knowledge that can be employed to support the optimized decision-making of product lifecycle management. In this paper, a framework for Big Data driven product lifecycle management was proposed to address these challenges. Within the proposed framework, the availability and accessibility of data and knowledge related to lifecycle can be achieved. A case study was presented to demonstrate the proof-of-concept of the proposed framework. The results showed that the proposed framework was feasible to be adopted in industry, and can provide an overall solution for optimizing the decision-making processes in different phases of the whole lifecycle. The key findings and insights from the case study were summarized as managerial implications, which can guide manufacturers to ensure improvements in energy saving and fault diagnosis related decisions in the whole lifecycle. [ABSTRACT FROM AUTHOR]

This article discusses the potential to transfer big data algorithms developed for ‘predictive policing’ to the field of corporate sustainability. To do so the paper starts with the thought experiment asking if major corporate scandals with disastrous environmental (BP Oil spill) or social (Rana Plaza building collapse) consequences, or global warming and floods could be prevented if big data driven predictive algorithms were in place. The article reviews first efforts to utilize big data for promoting sustainability and for reducing harm. By analogy the concept of ‘predictive policing’ is identified to be transferred to a concept called “Predictive Sustainability Control”. A systematic literature review on predictive policing is conducted. In a next step all parameters, characteristics, functional areas and processes, as well as legal and ethical issues of predictive policing were clustered and presented. Subsequently the concept is developed out of the clustered themes and criteria (Table 1) and a definition is provided: Predictive Sustainability Control is the use of analytical techniques to identify subjects for mutual deliberation, supervision and intervention with the goal of preventing future harm related to environmental, social and governance issues, solving past scandals, and identifying potential actors/corporations of unsustainable activities and their stakeholders in the near future. Furthermore three functional areas ( sustainability management, stakeholder partnership and regulatory integration ) are defined and the concept is operationalized in a big data driven environment (Fig. 1). In the operationalization the concept of a digital ‘planetary nervous system’ is proposed and eXtensible Business Reporting Language data repositories used in corporate data management and Corporate Social Responsibility reporting were integrated to arrive at a data set, where predictive analytics could be applied to prevent future harm and reduce current unsustainability. In conclusion the question of governance, data protection and privacy is discussed critically and future research avenues for theory advancement and empirical testing are presented. In closing limitations of legality and functionality are discussed. The overall scientific value can be seen in the potential, which big data and algorithms have also for promoting and enforcing sustainable development based on rigorous data management leading to the predictive identification of likely unsustainable events. [ABSTRACT FROM AUTHOR]

China is actively promoting low-carbon city development. Understanding the perception of a low-carbon city by the entire society is vital for effective policy implementation. However, the essence of a “Low Carbon City” is not consistently understood by the society. In this article, big data mining and a bibliometric approach were adopted to establish several indicators so that scholars (based on literature) and the public (based on social network) perceptions on low carbon cities can be uncovered. The major findings are as follows: The numbers of literature and social networking posts with “low carbon” & “city” as key words have increased during the period 2010–2016 and reached a peak in 2013. The literature mainly defines low-carbon cities from a macro-economic aspect. However, the public express their concerns with respect to the quality of life or how their behaviors could be affected. Cities with high PPI (Publication Popularity Indicator) or WPI (Weibo Popularity Indicator) values are mainly clustered in the three Chinese main economic regions (Jing-Jin-Ji region, Yangtze River delta region and Pearl River delta region). Cities with high CPII (Comprehensive Popularity Intensity Indicator) values are not always clustered in the three main economic regions but are located dispersedly. This implies that cities can make great progresses in low carbon development irrespective of their economic levels and spatial locations. Three cities (Shuangyashan, Chongzuo and Guyuan) had zero PPI , WPI and CPII values, reflecting that they have not been considered for low carbon development by the scientific community or the public. It is obvious that some cities have been ignored in the context of low carbon development or they have not taken any initiatives with respect to low carbon development. The CPII used in this paper could be a useful indicator for monitoring and evaluating the perception of low carbon development at the city level. [ABSTRACT FROM AUTHOR]

Although the existing studies have paid much attention to the issue on the impacts of air pollution on public health, they fail to consider the spatial spillover effect and externality of air pollution as a significant influencing factor of public health, causing the biased results. This paper employs a spatial Durbin model (SDM) to investigate the impacts of both air pollution and its spatial spillover effect on public health in 116 cities of China over 2006–2012. We use the survey data of lung cancer mortality and respiratory diseases mortality from 161 sites of mortality censoring spots in China to measure public health and use the statistical data of industrial emissions of sulfur dioxide and soot from the corresponding cities to measure air pollution. Compared with the existing studies, from the perspective of the scale and scope of dataset, our dataset can be regarded as a big data sample in the fields of environmental and health economics. We estimate the direct and indirect effects of air pollution to distinguish the local and neighbors' impacts of air pollution on the mortalities caused by the two diseases. The empirical results provide the evidence for the adverse impacts of air pollution and its spatial spillover effect on public health. On average, an increase of ten thousand tonnes in industrial sulfur dioxide emissions in certain city will lead to an increase of 0.035 and 0.03 per ten thousand persons in local mortalities from lung cancer and respiratory diseases, respectively, and a total increase of 0.217 and 1.543 per ten thousand persons in the mortalities of all its neighbors from lung cancer and respiratory diseases, respectively. On an annual average, due to the air pollution, more than 200 billion RMB are spent on the medical treatment for curing health damages, and approximate hundred thousand lives were taken away. [ABSTRACT FROM AUTHOR]

Driving restrictions are often introduced as an urban management method for reducing the air pollution and traffic congestion by prohibiting drivers from using their vehicles on given days, based on the last digits of vehicles’ license plates. A number of cities in developing countries have used driving restrictions. This paper proposes an empirical strategy to estimate the effects of driving restrictions on air quality. The strategy combines internet based semi-structured data source, big data processing procedure, and different estimation methods of regression discontinuity design to carry out the analysis. The results show that the driving restrictions are effective for short-run period, but are ineffective for long-run time period. Heterogeneity in short-run effect for different cut-points has also been observed. The results of the study are helpful in understanding the effectiveness of current driving restriction policies and to improve these further to achieve better results in future. [ABSTRACT FROM AUTHOR]

In recent years, many iron and steel enterprises (ISEs) have begun to implement cleaner production (CP) technologies in order to improve environmental performance and competitive advantage. We recognize that the evaluation of ISE CP performance is becoming more important than ever. It is for this reason that we propose an approach for evaluating such performance in this paper. Different from most of the traditional methods, our proposed approach directly involves the competitive relationship among ISEs in the context of the availability of big data today. On the basis of the evidential reasoning (ER) approach and the data envelopment analysis (DEA) cross-efficiency concept, we first construct a nonlinear programming model to portray the competitive relationship among ISEs, and obtain the optimal weight and the optimal utility value. Then, by applying the ER approach to the aggregate evaluation information, we obtain the ranking of the ISE CP performance. Finally, we use a numerical example to illustrate the detailed implementation process of the proposed approach and demonstrate its potential applications. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between landscape diversity and crop production using big data techniques. In the case study area of Hebei Province, China, there is a positive ecological effect of landscape diversity on crop production (coefficient of H (Shannon's index) and H 2 on crop production are 7.9665 and −2.2388 respectively), and a negative effect via operating cultivated land change (coefficient of H and H 2 on cultivated land change are −5.4253 and 1.5520 respectively). This negative effect is measured with big data techniques and is explained by variables such as the ratio of cultivated land and other basic local conditions. The net effect of landscape diversity on crop production is negative, all else the same, reflecting the strength of the impact through cultivated land change. Thus, it is important to adhere to a certain level of landscape diversity if crop production is to be sustained. [ABSTRACT FROM AUTHOR]

Tour activities are closely related to carbon emissions. Low-carbon tourism is an important means of saving energy and reducing emissions. With the advent of big data mining technology, it is urgent foracademia to discuss the measurement of self-driving tour carbon emission flows and its spatial relationship with scenic spots based on big data on traffic. This paper measures self-driving tour carbon emission flow data from 2014 and analyzes its spatial relationship with the scenic spots using data mining technology and the tour traffic carbon emission flow analysis method. Results show that (1) the regions that have high expressway traffic and self-driving tour traffic are mostly concentrated along the Yangtze River, while the regions that have low expressway traffic and self-driving tour traffic are concentrated in north Jiangsu. For regions that have high self-driving tour traffic, they are spatially concentrated in Nanjing, Suzhou, Wuxi and Changzhou. (2) The self-driving tour carbon emission flow totals 0.52 Mt in Jiangsu, the top five carbon emitters are the routes from downtown Nanjing to downtown Nanjing, downtown Suzhou to downtown Suzhou, downtown Suzhou to downtown Wuxi, downtown Changzhou to downtown Nanjing, and downtown Suzhou to downtown Nanjing. The total number of carbon emissions from these routes account for 17.25%, making them a major source of carbon emissions in Jiangsu. (3) Carbon emissions from self-driving tours do not have a significant positive correlation with the grades of the scenic spots. That is, high carbon emission flows from self-driving tours may happen to both high-grade and low-grade scenic spots. [ABSTRACT FROM AUTHOR]

The environmental problem and global warming have drawn many governments’ attention. In order to solve the problem, reasonable allocation of carbon dioxide emission (CDE) permits is an important issue because it sets the targets for the decision making units to control the amount of carbon dioxide emission. In this paper, a new data envelopment analysis approach is proposed to evaluate the efficiency of decision making units in a big data environment and set the carbon dioxide emission permits for each decision making unit with the minimum costs. Comparing with the previous works on carbon dioxide emission permits allocation, we firstly allocate the amount of carbon dioxide emission permits taking account into the cost each decision making unit should take to achieve its target. Moreover, two kinds of linear programming problems are developed under efficiency invariance principle and efficiency changeability principle respectively. As the allocation of carbon dioxide emission permits causes the minimum cost, the decision making units are more easily to accept the allocation alternative. Finally, our approach is applied to the carbon dioxide emission permits allocation among Chinese provinces. The results show that the approach can well allocate the CDE permits with the minimum cost, and also reveal the relationship between the marginal cost of reducing CDE permits and the adjustment on them. [ABSTRACT FROM AUTHOR]

China's economic has got remarkable achievements during the past 30 years, which has brought tremendous pressure on natural resources and serious environmental pollution at the same time. In this paper, we aim to make environmental efficiency analysis of China's regional industry. Data envelopment analysis (DEA) method is incorporated and the slacks-based measure (SBM) model considering undesirable outputs is introduced to measure the environmental efficiency of different regions. Big data theory is introduced in the collection and selection of the input and output data of the regions. Further, we evaluate the environmental efficiencies of China's industry using data from 2008 to 2012. The results show that apart from several developed provinces, the environmental efficiencies of China's industry are generally low. And the environmental efficiencies of the regions did not show any increasing trend through the past 5 years. Additionally, lager differences exist in environmental efficiencies between the regions in China. Finally, we suggest the Chinese government to focus on the low environmental efficiencies and the unbalanced development of its regional industry. [ABSTRACT FROM AUTHOR]

The Poyang Lake Eco-Economics Zone is a national zone that has been approved by State Council as part of China's national strategy for sustainable development in the largest lake area since 2009. In this study, we aim to examine its sustainability characteristics, including environmental efficiency, the shadow price of pollutants, and substitutability among inputs and outputs, so that to improve the environmental regulation in this area. For this purpose, we use the global non-radial directional distance function and its duality theory. By using the envelope model, the environmental efficiency indicator can be calculated. By employing the dual model of the non-radial directional distance function, the shadow prices and Morishima elasticities of substitution among inputs and outputs can be derived. We use the proposed method to conduct an empirical study of the Poyang Lake Eco-Economics Zone at the prefecture-level during the period from 2009 to 2013. Results show that many regions in the Poyang Lake Eco-Economics Zone are not performing well on environmental efficiency and have potential to improve by 31.9% on average. Meanwhile, the waste water has much lower shadow prices than the either sulfur dioxide or soot. On the other hand, with regard to the shadow prices of soot emission, approximately one-third of these regions were not charged anything for disposing soot emissions at any time of day or night during the 2009–2013 periods. Moreover, these 38 regions show energy-intensive as well as labor-intensive features. Finally, this paper also discusses the policy implications, including establishing the pollutant emissions trading system in China as well as encourage the technology innovation for energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

China's economic reform and opening policy has resulted in China's rapid growth, especially in terms of economy. The growth, however, is mainly based on huge consumption of natural resources, and that consumption should be reduced by significant amounts to protect global ecosystems. Emerged in 1980s, “big data” has developed very quickly in recent years, bringing new perspectives and opportunities for all kinds of academic fields, especially in DEA since it is a research highly based on data. In this paper, we propose a SBM-DEA model based on natural resource input orientation to evaluate the efficiency of natural resource utilization for 26 provincial regions in mainland China from 2005 to 2012. A DEA-based approach is developed to allocate a reduced total of natural resources among the 26 provincial regions. Drawing support from the developments of “big data”, we use each provincial region's previously observed production data to characterize its own production technology, which allows a better analysis of how the production will change after allocations of natural resources are reduced. Our empirical study shows that most provincial regions perform well in terms of efficiency in utilizing natural resources, but that is based on huge consumption of natural resources. From an area perspective, the eastern area of China achieved the highest average efficiency value, followed by the central and western areas. Our study also indicates that the most effective allocation of the natural resources requires about half of the 26 provincial regions to reduce their natural resource consumption while the remaining ones can maintain their original consumption. [ABSTRACT FROM AUTHOR]

This paper addressed the issue of the allocation of emission permits (AEP) in large data sets, with the goal of providing government strategies to practically operate the AEP in a group of organizations, and realize economic, social and environmental goals at the same time. We propose a robust multi-criteria AEP approach, together with its tractable algorithm, by extending the classical theory of data envelopment analysis (DEA) for large data sets. Reasonable AEP mechanisms adjusted to the large data set can be derived from this approach. The main advantages of this approach are as follows. First, this approach shows real-world tractability of large data sets, as it takes the characteristics of large data sets into full consideration. Second, the proposed AEP mechanism can help centralized decision makers to achieve the lowest total group-level emission while keeping group-level outputs invariant, and the mechanism is proved to be sustainable theoretically. Third, besides obtaining an optimal allocation plan for emission permits, the proposed approach can be used to calculate the optimal emission standard and optimal total amount of permits to be allocated. The proposed approach was used in an empirical study of SO 2 emission permits allocation among 202 prefecture-level cities in mainland China. The results further demonstrated theoretical and practical values of our method. One valuable policy suggestion resulted from the empirical analysis is presented as well. [ABSTRACT FROM AUTHOR]

Cleaner production (CP) is considered as one of the most important means for manufacturing enterprises to achieve sustainable production and improve their sustainable competitive advantage. However, implementation of the CP strategy was facing barriers, such as the lack of complete data and valuable knowledge that can be employed to provide better support on decision-making of coordination and optimization on the product lifecycle management (PLM) and the whole CP process. Fortunately, with the wide use of smart sensing devices in PLM, a large amount of real-time and multi-source lifecycle big data can now be collected. To make better PLM and CP decisions based on these data, in this paper, an overall architecture of big data-based analytics for product lifecycle (BDA-PL) was proposed. It integrated big data analytics and service-driven patterns that helped to overcome the above-mentioned barriers. Under the architecture, the availability and accessibility of data and knowledge related to the product were achieved. Focusing on manufacturing and maintenance process of the product lifecycle, and the key technologies were developed to implement the big data analytics. The presented architecture was demonstrated by an application scenario, and some observations and findings were discussed in details. The results showed that the proposed architecture benefited customers, manufacturers, environment and even all stages of PLM, and effectively promoted the implementation of CP. In addition, the managerial implications of the proposed architecture for four departments were analyzed and discussed. The new CP strategy provided a theoretical and practical basis for the sustainable development of manufacturing enterprises. [ABSTRACT FROM AUTHOR]

Carbon Capture and Storage is the only technology available to mitigate large-scale greenhouse gas emissions from fossil fuel based power and industrial sectors in the near future. When technology to capture carbon dioxide (CO 2 ) is relatively mature and commercially available for power and industrial sectors, safe, reliable and long-term storage of captured CO 2 remains a key uncertainty affecting wide-spread deployment of Carbon Capture and Storage technology yet. In this paper, the authors assessed techno-economic aspects of geological CO 2 storage options, from CO 2 transportations, various geological storage approaches, to CO 2 leakage monitoring. Compared with depleted oil/gas reservoirs and coal seams, deep saline aquifers possess much larger storage capacities and may be possibly near many CO 2 emission sites due to widespread distributions. If CO 2 storage is combined with enhanced industrial production (e.g. oil, natural gas), it has a greater potential to reducing the overall cost of CO 2 storage. Potential CO 2 leakage may be the main barriers to the development of CO 2 geological storage. It is recommended to make full use of big data mining approach in selection and approval of CO 2 geological sites, estimation of storage capacities, assessment of potential leakage risks, awarding of carbon credits, as well as analysis of public acceptations. At the same time, as a leakage-free CO 2 storage option, CO 2 mineralization & industrial utilization is to trap CO 2 permanently in stable minerals by reactions with metal oxides and forming stable carbonates. These CO 2 mineralization & industrial utilization schemes need to guarantee sustainable or environmentally friendly processes and satisfy basic principles of industrial ecology if implemented on a large industrial scale. Currently, most of CO 2 storage schemes are still in the early stage of technological development and are still far from large-scale commercialization. The high cost, high energy penalty, safety and reliability, and policy uncertainties are main barriers for the implement of carbon storage schemes. [ABSTRACT FROM AUTHOR]

The theme of this Special Volume (SV) focuses on improving natural resource management and human health to ensure sustainable societal development. Natural resources have been exploited unduly regardless of the consequences, which has resulted in inappropriate management natural resources and has caused severe environmental degradation. Contributions in this SV addressed improved environmental management, utilization, and allocation of natural resources; evaluation of sustainable natural resource management; pollution prevention and treatment; and evaluation and suggestions for improved natural resource-related policies. The authors presented an inspiring panorama of the initiatives that have been developed throughout the world for sustainable natural resource management and improve societal development. Theoretically, new approaches to bridge the gaps between the economic development and environmental protection were increasingly dominant. Empirically, many of the papers provided case studies of regions in China and other regions. The authorship reflected growing collaboration between researchers from many different countries or universities. While the great diversity of contributions on the topic reflected the wealth of insights generated on the topic in recent years, there is much more that must be done to achieve societal sustainability in natural resource management. [ABSTRACT FROM AUTHOR]

Remanufacturing is deemed to be an effective method for recycling resources, achieving sustainable production. However, little importance of remanufacturing has been attached in PLM. Surely, there are many problems in implementation of the remanufacturing strategy, such as inability to effectively reduce uncertainty, lack of product multi-life-cycle remanufacturing process tracking management, lack of smart enabling technology application in the full lifecycle that focusing on multi-life-cycle remanufacturing. After analyzing the reasons, through integrating smart enabling technologies, a new PLM paradigm focusing on the multi-life-cycle remanufacturing process: Big Data driven Hierarchical Digital Twin Predictive Remanufacturing (BDHDTPREMfg) is proposed. And the definition of BDHDTPREMfg is proposed. A big data driven layered architecture and the hierarchical CPS-Digital-Twin(CPSDT) reconfiguration control mechanism of BDHDTPREMfg are respectively developed. Then, this paper presents an application scenario of BDHDTPREMfg to validate the feasibility and effectiveness. Based on the above application analysis, the benefits of penetrating BDHDTPREMfg into the entire lifecycle are demonstrated. The summary of this paper and future research work is discussed in the end. [ABSTRACT FROM AUTHOR]

Self-reported life satisfaction of China's population has not improved as much as expected during the economic boom, which was accompanied by a significant decline in environmental performance. Is environmental pollution the culprit for the lagging subjective well-being? To explore this issue, this paper adopts sentiment analysis to construct a real-time daily subjective well-being metric at the city level based on the big data of online search traces. Using daily data from 13 Chinese cities centred on Beijing between August 2014 and December 2019, we look at the corelation between subjective well-being and air pollution and the heterogeneity in this relationship based on two separate identification strategies. We find that air pollutants are negatively correlated with subjective well-being, and well-being tends to decline more from pollution during hot seasons. In addition, residents in wealthier regions tend to be more sensitive to air pollution. This result may be explained by the differences in the subjective perception of air pollution and personal preferences at different levels of income. These findings provide information about concerns of the public, thereby helping the government to take appropriate actions to respond to the dynamics of subjective well-being. • Sentiment analysis and big data are adopted to construct a daily city-level subjective well-being metric. • Higher levels of airborne particulates lead to a decline in subjective well-being. • Peoples' well-being tends to decline more from pollution during hot seasons. • Residents in wealthier regions tend to be more sensitive to air pollution. [ABSTRACT FROM AUTHOR]

This research takes manufacturing enterprises with big data capability as the research object, based on organizational decision-making theory and planned behavior theory, the mediation effect model of "capability-intention-behavior" is constructed. It uses econometric models to explore the impact and conduction process of big data capability on green process innovation. The results show that big data capability have an obvious direct effect on green process innovation. By examining the mediation effect test, we find that green innovation intention partially mediates the relationship between big data capability and green process innovation. Then a fuzzy set qualitative comparative analysis (fs QCA) is used in this paper to analyze the causal complexity between big data capability and green process innovation, and to identify six effective configuration paths. According to the results, the synergy of big data acquisition capability, big data analysis capability and big data insight capability is crucial for high green process innovation, where big data insight capability plays a central role and big data acquisition capability or analysis capability and green innovation intention play a marginal role. According to the heterogeneity test, it is found that big data capability has stronger roles in the eastern region in China; green innovation intention has a stronger impact in the central and western regions in China. As enterprise-scale increases, the impact of big data capability on green process innovation increases and then declines, while the impact of green innovation intention on green process innovation continues to decline. [Display omitted] • Dividing big data capability into acquisition, analysis and insight capabilities. • Big data capability affects green process innovation via green innovation intention. • Constructing the intermediary effect model of "capability-intention-behavior". • Fs QCA is used to analyze the causal complexity and identify the configuration paths. [ABSTRACT FROM AUTHOR]

In a shopping mall, it's a common phenomenon that brands enter and exit. Studying the brand's entry and connection in the mall's ecological chain is an important factor in judging whether the brand can develop sustainably in the mall. Based on the analysis of 724,685 consumers' consumption data from one International Shopping Center in Shanghai, China, this paper conducts the social network analysis, builds a brand network map, and examines brand connection through visual analysis and cohesion subgroup analysis. Empirical results indicate that brand awareness, product category, brand positioning, and consumer characteristics are the major factors that influence brand connection. The sensitivity analysis is further conducted based on the singular value decomposition (SVD). Furthermore, these four factors have a positive impact on the sustainable development of the brand after entering and a mathematical model is established to determine whether a brand should be settled in a certain shopping mall. This research is the first to study the factors affecting the sustainable development of brands in the shopping mall ecological chain. Using big data technology to provide brand selection guidance for shopping malls from a micro perspective. [ABSTRACT FROM AUTHOR]

This paper studied greenhouse gas inventory data acquisition and analytics for municipalities in Thailand. A complete and transparent GHG inventory of eight municipalities was developed to document the current situation, and to help decision-makers to clarify their priorities for reducing greenhouse gas emissions. The Global Protocol for Community-Scale Greenhouse Gas Emissions Inventories guidelines was used to investigate and calculate the greenhouse gas emissions and assess data accuracy. The results indicated that the data source, data format, and data collection of each municipality are relatively similar. Moreover, the activity data needed to be obtained from several authorities. The results showed that Nonthaburi Municipality had the highest greenhouse gas emissions at 2,286,838 tCO 2 e/yr and Buriram Municipality, the lowest at 239,795 tCO 2 e/yr. On a per-capita basis, Lamphun Municipality was the highest with 10.1 tCO 2 e/capita and Buriram Municipality the lowest with 3.8 tCO 2 e/capita. The results suggest that the municipalities should continually develop a GHG database by creating a routine procedure. An information management system should be produced in the shape of big data which can lead to state policies, plans, and actions for city development to ensure the reduction of greenhouse gas emissions. This in turn will lead to a low carbon city. [ABSTRACT FROM AUTHOR]

Because the volume of food waste is increasing, actions are required to mitigate the environmental and social impact of food waste generation. This paper investigates the business models of 41 selected startups (technology and service providers) to capture how companies avoid food waste or use food waste as a resource. The case study analysis, based on secondary sources, shows that some startup business models leverage Big Data Analytics (BDA) to avoid food waste by optimizing an existing linear supply chain, while other business models create value out of food waste by leveraging a circular food supply chain. We found that the latter business models are not fully exploiting the potential of BDA. Based on our findings, we derive three propositions and one corollary. Whereas BDA seems a necessary requirement for business models that are focused on optimizing a linear supply chain, it appears optional for business models closing the supply chain loop. The propositions also discuss the timing when startups should start developing BDA capabilities depending on the business model type. [ABSTRACT FROM AUTHOR]

With the development of the integrated energy Internet, energy structure optimization and emission reduction have led to higher requirements for developing various energy sources to enable coordinated and sustainable development. However, data-mining methods are rarely used to study the coordination of multi-energy generation in published research results. In this study, from the perspective of power industry emissions, coordinated generation of various energy sources, and balance of power generation and consumption, a data-mining algorithm was used to analyze the development of thermal power, hydropower, wind power, waste heat, gas, and other power sources. The chi-square automatic interaction detection tree (CHAID), logistic regression, and two-step clustering methods were applied. The results show that: a) CO 2 and SO 2 emissions were mainly affected by thermal power generation, whereas NO x emissions were jointly affected by thermal power, garbage power, and gas-fired power, and the emissions of various pollutants increased with an increase in power consumption. The optimal power-generation scheme under minimum emission can be obtained. b) There was a strong correlation between thermal power generation and residential electricity consumption, and renewable energy (wind energy, photovoltaic, hydropower) exhibited the highest correlation with the electricity consumption of the tertiary industry, which indicates that renewable energy generation can be promoted by managing electricity consumption in the tertiary industry. c) When the electricity demand of all users was small, the proportion of renewable energy power generation increased; in contrast, the thermal power generation was larger. This indicates the importance of improving the sustainable and stable power supply of renewable energy. This study provides a data analysis model for the coordinated development of multiple energies, which will contribute to the decision-making basis for controlling power emissions, improving the utilization rate of renewable energy, and optimizing the energy structure. [Display omitted] • Rich data (8760 h): multi-energy power generation, consumption and emission. • Introduces a complete big data analysis framework to study multi-energy generation. • Three data mining methods are used to analyze power supply, demand and emission. • The conclusions of this paper have a positive effect on energy structure. [ABSTRACT FROM AUTHOR]

Dockless bike sharing (DBS) provides a sustainable and green travel mode, which also enhances the connections with other travel modes. Understanding the travel mobility and demand of DBS become an urgent task for government and operators to provide better service. In this paper, we propose a network-based method to detect the travel mobility of DBS users based on the actual trip data. The studied area is divided by square grid with same size. The grids with trips are considered as nodes and the connections between nodes are considered as edges. To gain the dynamic characteristics of DBS travel mobility, we construct several networks according to different time periods in a weekday. We build a data-driven framework to analyze DBS network including accessibility, spatial inequality, spatial autocorrelation and network-based indicators. The relationship between flow strength and point-of-interest (POI) is discussed. The results show that travel demands of DBS are higher in morning peak and evening peak on weekdays. The DBS networks are inequality, connections are concentrated on center area. From the network view, the DBS network are assortative and positive autocorrelated with evident communities. The results imply that the number of residence and transport facility have strong correlations with flow strength. • The spatial-temporal characteristics of bike-sharing was explored. • Complex network of bike-sharing was constructed based on grid divisions. • The relationship between travel demand and POI was tested. • DBS networks are assortative and positive autocorrelated with evident communities. • The number of residence and transport facility have strong correlations with flow strength. [ABSTRACT FROM AUTHOR]

Campus bike-sharing is widely recognized as a popular transportation product for college students to achieve pro-environmental mobility. However, unsuccessful cases of campus bike-sharing are repeatedly reported in China in recent years, and previous studies rarely offer insights on these failures. Therefore, this paper places a specific study regarding the unsuccessful practice in Dalian Maritime University, China. Using the survey data, we investigate the key factors that affect the bike-sharing adoption of college students through Structural equation modelling and Index score of variables. Other than the conclusions that in line with previous researches, our results indicate that: 1) The service quality dominates the positive effects on college students' adoption intention of bike-sharing, but its index score is relatively low. Thus, more resources should be concentrated to promote the service quality. The timely maintenance, the ride comfort and the better customer service are the key aspects need to be further improved; 2) The access cost, especially the high deposit, is surprisingly found the main barrier to bike-sharing usage. These findings suggest that cancelling the deposit and employing credit rating based on big data instead, can be a win-win strategy to protect the security of transactions as well as further secure the patronage of college students. In addition, several other policy implications are also offered for service providers to enhance bike-sharing competitiveness. • Research on an unsuccessful practice of campus bike-sharing in a Chinese college. • Perceived social value promote students' adoption of campus bike-sharing. • The high deposit fee is the main barrier that impacts college students' participations. • Governing irregular parking behaviors is the key to improve bike-sharing service quality. [ABSTRACT FROM AUTHOR]

The building sector is a major source of energy consumption and greenhouse gas emissions in urban regions. Several studies have explored energy consumption prediction, and the value of the knowledge extracted is directly related to the quality of the data used. The massive growth in the scale of data affects data quality and poses a challenge to traditional data mining methods, as these methods have difficulties coping with such large amounts of data. Expanded algorithms need to be utilized to improve prediction performance considering the ever-increasing large data sets. In this paper, a preprocessing method to remove noisy features is coupled with predication methods to improve the performance of the energy consumption prediction models. The proposed preprocessing method is based on the well-known principal component analysis (PCA) and treats the historical meteorological and energy data of buildings. The cleaned and processed data are used in five prediction models including linear regression, support vector regression, regression tree, random forest and K-nearest neighbors. The proposed methodology is applied to four case studies with different climate zones (cold, mild, warm-dry and hot-humid) to study the effect of dataset patterns on the feature reduction and prediction performance. The results show that the proposed method enables practitioners to efficiently acquire a smart dataset from any big dataset for energy consumption prediction problems. In addition, the best prediction model for each climate zones with considering mean square error, R2, residual values and execution time is proposed. Image 1 • Principal component analysis (PCA) removes noisy features of building datasets. • PCA is integrated with five prediction models to improve prediction performance. • Four building energy datasets are used to assess the performance of the method. • Most efficient PCA-based prediction models are proposed for each building dataset. [ABSTRACT FROM AUTHOR]

The notable increase in artificial night light (ANL) induced by the rapid urbanization process has been widely studied, but a deep understanding of the supply and demand status of ANL is still lacking. This paper attempts to map the supply and demand of ANL from the human perspective by using advanced Loujia1-01 nighttime imagery and social media derived population density (PD) data, which provides a new tool for light regulation in urban management. The bivariate clustering based k-means algorithm and template matching technique are integrated to delineate mismatch regions at the block scale to further analyze the underlying reason for unbalanced status. The results showed that the high supply but low demand (HSLD) ANL status was the leading component in the mismatch regions, occupying more than 650,000 ha and mainly occurring in the city center. The HSLD proportion was considerable in terms of public services (44%), commercial (40%), industrial (39%), transportation (56%), and green space areas (53%). Moreover, the HSLD area notably increased 946 ha over time from 18:00 to 22:00. The measurements for validation obtained by field investigation showed highly linear relationship with ANL (R2 = 0.75) and PD (R2 = 0.62), and the mapping results were consistent with the actual conditions. This study reveals the highly unbalanced ANL status, and appeals to planners for the establishment of optimal lighting regulations to alleviate disruptive effects. Image 1 • The supply and demand status of ANL in open areas is mapped. • The relationship between ANL and PD has been quantified. • The significant HSLD status of ANL calls for high attention. • Unbalanced status of ANL is highly related to housing price. • Light regulation is necessary to alleviate the disruptive effects. [ABSTRACT FROM AUTHOR]

Big data applications, from its inception have experienced unprecedented changes in technological revolution and development. The need of the hour is to use big data to break through the incompatible bottleneck of environment protection and development. Therefore, this paper constructs a theoretical model to evaluate the changes of relative benefits big data have on R&D through "substitution effects", "complementary effects", and further lead to directed technical change and its impact on the environmental quality. For better understanding the role of big data, we compare different impacts on environmental quality both by considering the big data application and by ignoring it. In addition, we analyze the required environmental regulation policy adjustments under the influence of big data. The results show that: (1) By improving the relative benefits of clean technology R&D, the application of big data further enhances the quality of the environment. (2) Mere relying on the application of big data alone cannot prevent environmental disasters, but it must be supplemented with conducive environmental regulation policy to achieve the best results. (3) The application of big data can reduce the "subsidy" for clean technology R&D to avoid environmental disasters, while the impact of big data on "environmental taxes" varies with the development of clean technology levels. The results of this research will help to clarify the positive effects of big data on environmental quality and implement reasonable environmental regulation policy. • Big data as a factor of production and can endogenize into the production function. • The model constructed by substitution effects and complementary effects on big data. • Big data leads to directed technical change, and improves environmental quality. • Relying on big data alone cannot prevent environmental disasters. • Big data reduces the subsidy, while taxes vary with different clean technology levels. [ABSTRACT FROM AUTHOR]

The circular economy plays an important role in energy-intensive industries, aiming to contribute to ethical sustainable societal development. Energy demand response is a key actor for cleaner production and circular economy strategy. In the Industry 4.0 context, the advanced technologies (e.g. cloud computing, Internet of things, cyber-physical system, digital twin and big data analytics) provide numerous opportunities for the implementation of a cleaner production strategy and the development of intelligent manufacturing. This paper presented a framework of data-driven sustainable intelligent/smart manufacturing based on demand response for energy-intensive industries. The technological architecture was designed to implement the proposed framework, and multi-level demand response models were developed based on machine, shop-floor and factory to save energy cost. Finally, an application of ball mills in a slurry shop-floor of a partner company was presented to demonstrate the proposed framework and models. Results showed that the energy efficiency of ball mills can be greatly improved. The energy cost of the slurry shop-floor saved approximately 19.33% by considering electricity demand response using particle swarm optimisation. This study provides a practical approach to make effective and energy-efficient decisions for energy-intensive manufacturing enterprises. • A framework of data-driven sustainable intelligent manufacturing is proposed. • The framework is to achieve a circular economy for energy-intensive industries. • Multi-level demand response models based on PSO are developed to save energy costs. • The framework and models can benefit the environment, economy and society. [ABSTRACT FROM AUTHOR]

In this paper, a chain-structure echo state network (CESN) is newly proposed to enhance the scalability, robustness and computational efficiency in spatio-temporal solar irradiance prediction. Firstly, the network structure, mathematical model, stability analysis, and training mechanism are studied for CESN. Secondly, according to the spatial correlation analysis, the number of ESN modules in CESN can be determined, such that the dynamics of different features can be fully fitted. In addition, the autocorrelation analysis is adopted in temporal information of each spatial variable to provide appropriate inputs for each ESN module. Finally, the spatio-temporal solar irradiance prediction model is established based on CESN. Simulation results illustrate that the CESN could achieve more accurate prediction, compared with backpropagation (BP) and Elman neural networks, classical ESNs. [ABSTRACT FROM AUTHOR]