Search

Over the last two decades global economy and financial markets have seen many crises with an increasing frequency. Many of them were either unexpected or their effects were unpredictable. Therefore, a lot of conventional and popular investment opportunities have shortened or became less attractive for either private or institutional investors leaving a room for new instruments and products to gain popularity. Unlike new volatile and uncertain markets like cryptocurrency, there are potentially more stable processes such as M&A activity, which can be predicted to earn abnormal returns. In this paper, we study various financial and non-financial indicators of acquired and non-acquired companies to provide a set of variables that can describe a company from different perspectives. Next, M&A prediction model is designed. Then, techniques are discovered to increase its explanatory and predictive power, and flexibility making it applicable for different economic environments without being harder to implement it by a potential user. In the end, its efficiency is measured on a real data to compare it with a result of methods found in earlier papers. [ABSTRACT FROM AUTHOR]

For policymakers who intend to make decisions on the policy to stimulate economic growth and enhance job opportunities, business demography statistics serve as important data sources for promoting entrepreneurship, which is a key for the policy. Business demography, which usually consists of annual demographic information such as the number of births, deaths, and survivals of businesses, can be produced from a business register in many countries. In Japan, however, it is difficult to produce business demography directly from the Japanese business resister called Establishment Frame Database because the major data source for the database is the Economic Census, conducted twice every five years. Alternatively, business demography can be estimated using the data of the Economic Census. However, it has been pointed out that short-lived births of businesses are hardly grasped by the estimation using the data of statistical surveys such as the Economic Census at intervals of more than one year. This paper introduces a stochastic model to cope with the above issue and proposes a calculation formula to estimate the number of short-lived births of businesses as well as other demographic indicators of business demography. Then the paper is followed by a numerical example, which proves to overcome the shortcomings of previous methodologies. [ABSTRACT FROM AUTHOR]

Statistical modeling and forecasting are very important for decision-making in any field of life. This paper has two major objectives, namely, statistical modeling and forecasting of real phenomena. For covering the first aim (i.e., statistical modeling), we introduce a new probabilistic model. The new model is introduced by mixing the Dagum distribution with the weighted T X family approach. The proposed model is called the weighted T X Dagum distribution and possesses heavy-tailed characteristics. The new model is illustrated by analyzing real-life data related to Bitcoin prices. To cover the second aim (i.e., forecasting), we take into account six macroeconomic and financial indicators to investigate their impact on Bitcoin prices such as the Adaptive least absolute shrinkage and selection operator (Alasso), elastic net, and minimax concave penalty. After analyzing the data, it is found that Alasso and MCP have retained all the included predictors, except import, while Enet holds all the predictors. The root means square error and mean absolute error associated with MCP are lower than Alasso and Enet, which reveals that MCP fits the data very well as compared to rival methods. [ABSTRACT FROM AUTHOR]

The medium- and long-term operation strategy of the hybrid power system needs to adapt to the continuous changes in the market. This paper presented a medium-term (monthly) operational simulation model and nested it into a long-term (yearly) optimization model. It was solved by the genetic algorithm (GA) to maximize yearly revenue. The planned delivered-electricity to power process constraints were present to improve the optimization efficiency. Using penalty price factor (α) and threshold of exempted punishment for the supply shortage δ to characterize the punishment for supply shortage risk. This framework was applied for a new hybrid power system so-called LCHES-WP (large-scale cascade hydropower energy storage system, wind power, PV power). It can participate in the electricity trading market with a bundled output pattern. The real cascade hydropower stations in Qinghai Province, China, are selected as the case study. In the baseline scenario, the current time of use (TOU) electricity price and punishment level (α = 0.05, δ = 5%) were considered. It reveals that (1) the revenue (baseline scenario, 6.15351 billion CNY) in the bundled output pattern is higher by 45.6% than in the separate output pattern. (2) the economic and technical indicators are sensitive to α , but not to δ. α increases make the punishment stricter, supply shortage risk was significantly reduced due to the probability of the shortage event, the maximum monthly supply shortage ratio and punishment electricity are reduced by up to 22.5 percentage points, 26.7 percentage points, and 1880.1 GWh, respectively. The yearly revenue is reduced by up to 4.7%. the reason is the yearly curtailment is increased and the penalty amount does not change dramatically with the changes in punishment. (3) The α = 0.6 and α = 1.8 can be regarded as demarcation point and upper boundary point for the case of hybrid power system, respectively. It is proposed that when planning a capacity scheme, stakeholders should consider that the demarcation point is higher than the current α. In general, this paper provides a reference for the medium- and long-term operation optimization of the LCHES-WP hybrid power system in the future. • Electricity settlement rule in medium and long term electricity trading is considered. • The monthly operational simulation model was nested in the yearly optimization model. • Bundled output pattern can rise by 45.6% of revenue than the separate output pattern. • The supply shortage risk is significantly reduced by increasing the punishment level. • The penalty amount does not change obviously with the changes in the punishment level. [ABSTRACT FROM AUTHOR]

Forecasting international trade flows is crucial for understanding global economic activity and serving as critical economic indicators used by economists and policymakers with significant implications for respective countries' economic policies. However, this is a challenging task due to the complexity of the relationships between entities involved in international trade. While several approaches have been proposed, knowledge graph-based models have emerged as promising solutions for predicting international trade flows. In this paper, we introduce a synthetic triple-generation algorithm for enhancing downstream tasks in knowledge graph embeddings based on the graph complement. The algorithm identifies missing relationships between entities by exploiting the complement graph and generates high-quality synthetic triples that improve the accuracy of predictions. We validate the generated triples using several knowledge graphs embedding methods and computing metrics. To perform an initial result screening, an international trade scenario is explored, demonstrating the effectiveness of the proposed approach in enhancing the performance of knowledge graph-based models for predicting international trade flows. [ABSTRACT FROM AUTHOR]

• Identification of important indicators for process safety. • A comparative study of process safety indicators in developing and developed countries. • Measuring the relative importance of the indicators using the Fuzzy Best Worst Method. Introduction: The importance of safety in high-risk industries such as oil and gas facilities has been reported previously. Process safety performance indicators can provide insight into improving the safety of process industries. This paper aims to rank the process safety indicators (metrics) by Fuzzy Best-Worst Method (FBWM) using the data gathered through a survey. Method: The study uses a structured approach considering the UK Health and Safety Executive (HSE), the Center for Chemical Process Safety (CCPS), and the IOGP (International Association of Oil and Gas Producers) recommendations and guidelines to generate an aggregate set of indicators. It calculates the level of importance of each indicator based on the opinions of experts from Iran and some Western countries. Results: The findings of the study demonstrate that some lagging indicators such as the number of times processes do not proceed as planned due to insufficient staff competence and the number of unexpected disruptions of the process due to failure in instrumentation and alarms are important in process industries in both Iran and Western countries. Western experts identified process safety incident severity rate as an important lagging indicator, whereas Iranian experts considered this as relatively unimportant. In addition, leading indicators such as sufficient process safety training and competency, the desired function of instrumentation and alarms, and proper management of fatigue risk play an important role in enhancing the safety performance of process industries. Experts in Iran viewed permit to work as an important leading indicator, while experts in the West focused on fatigue risk management. Practical Applications: The methodology used in the current study gives a good view to managers and safety professionals in regard to the most important indicators of process safety and allows them to focus more on important process safety indicators. [ABSTRACT FROM AUTHOR]

Traditional empirical models analyze impact of sentiments on financial market volatility using macroeconomic fundamentals or financial indicators. In this paper, recent methods of text based sentiment analysis of market from relevant news articles regarding economy and financial market are used. Two distinct market sentiments namely, positive and negative sentiments are constructed using different emotions which are identified through standard natural language processing methods. Further, the paper aims at proposing an augmented version of asymmetric GARCH model of conditional volatility for Indian stock exchange, Sensex, during the time period of April 19, 2007 to January 10, 2020 by incorporating aforementioned market sentiments. Empirical findings suggest dominant impact of negative market sentiment over positive one and it also provides evidence of noise trading in financially immature Indian stock market. [ABSTRACT FROM AUTHOR]

Increasing pressure from governments and stakeholders has motivated the study of sustainability assessment in the supply chain context at operational, tactical, and strategic levels. Several papers have been published during the last two decades, and the number is still rising. Although several authors present complex models that include environmental and social assessment, the applicability and usefulness of these works is often limited by lack of data availability and lack of consensus in what is to be measured on implementations of sustainable practices and strategies. This paper presents a systematic literature review of works addressing the supply chain network design (SCND) problem, in which at least two of the three dimensions of sustainability are assessed. This paper aims to identify indicators that are used when sustainability is evaluated in real applied cases. A total of 113 papers from 2015 to 2018 were selected, including documents studying forward, reverse, and closed loop supply chains (CLSC). Indicators in the economic, environmental, and social dimensions were classified according to an existing framework in the sustainable supply chain literature. The review finds a highlighted emphasis on environmental considerations; social criteria are still hardly studied. The study country origin analysis also shows an increasing concern for sustainable practices in developing economies, mainly in Asia. Finally, this paper presents a brief description of the areas where research opportunities exist, including sectors, measures, and methodologies to assess sustainability in the SCND problem. [ABSTRACT FROM AUTHOR]

In the digital era, digitalization brings great opportunities and challenges for cities' environmental governance and economic growth. However, it is worth noting that the impact of digitalization on cities' green transition has not been studied from the perspective of Metcalfe's Law. This paper uses the principal component method and non-radial directional distance function (NDDF) to calculate the digital development level and the green economic performance of 269 Chinese cities from 2003 to 2019. (1) The empirical findings demonstrate that digitalization has become a strong driver for cities' green economic transformation. (2) Digitalization indirectly promotes green transition through industrial structure optimization and green technological innovation. (3) The heterogeneity results show that the positive effect of digitalization on green development is more obvious in eastern and economically developed cities. (4) Using the partially linear functional-coefficient model (PLFC), this paper studies the non-linear nexus between digitalization and green economic transition from the perspective of Metcalfe's Law. Thus, Metcalfe's law is confirmed in the green transition of Chinese cities. Some policy recommendations are offered for realizing the coordinated development of digitalization and greening of the urban economy. [ABSTRACT FROM AUTHOR]

• With optimal market strategy and DTR, the annual revenue may be 204% of a base case. • Ideal power forecast may not correspond to the best commitment strategy. • Depending on market prices, NPV may be between −441 million € and +9.5 billion €. • Economic benefits of DTR are estimated between 0.7 and 1 billion euros. • Overplanting and DTR may reduce LCOE by 1 €/MWh. The overplanting of an offshore wind farm (OWF) with dynamic thermal ratings (DTR) is a promising solution for enhancing OWF performance. As the overplanted OWF generates an additional energy and DTR ensures its better transfer, the research question is if final OWF profits would be higher than associated costs. While there has been growing attention on this subject in recent years, there is still no research investigating the commitment strategies of overplanted OWFs with DTR. This paper investigates how commitment strategies may affect the economic performance of an overplanted OWF with DTR. The results show that, depending on the day-ahead commitment strategy, the annual revenue of OWFs may theoretically increase by up to 21% even without overplanting nor DTR, and by up to 204 % with overplanting and DTR. However, although commitment strategies, overplanting and DTR may significantly increase annual revenues of an OWF, its net present value (NPV) still heavily depends on market prices. In the presence of low market prices as in 2018, overplanting actually reduces the NPV of an OWF and, under the conditions considered here, keeps its NPV always negative. On the contrary, in the presence of high market prices as in 2022 or feed-in tariffs, the overplanting increases the OWF NPV. In the latter case, the NPV of the overplanted OWF is estimated between 1.5 billion and 9.5 billion euros while the discounted payback period is 3–12 years. The economic benefits of DTR for the overplanted OWF are estimated between 0.7 and 1 billion euros. The paper also shows that the optimal overplanting rate is very different whether the NPV or LCOE are considered. Finally, the paper shows that committing to the actual OWF power production (i.e. assuming a perfect power forecast) does not necessarily result in the highest revenue. [ABSTRACT FROM AUTHOR]

Internet satellite constellations are expected to play an important role in accommodating the rising global demand for internet access. Such rise in demand, however, is highly uncertain. Staged deployment is an approach that provides flexibility to tackle demand uncertainty by enabling the real option to reconfigure a constellation if demand changes. Advancements in satellite technology have led to the emergence of multi-layered constellations. This opens the opportunity to enhance staged deployment by enabling an additional real option: adding a new layer to a constellation. This real option has no associated reconfiguration costs, and therefore has the potential to reduce the cost of staged systems deployment. This paper proposes a framework to design multi-layer staged deployment systems and analyse their effectiveness in modern mega-constellations under global demand uncertainty. The framework is applied to four case studies based on market projections. Results show that multi-layer staged deployment decreases the expected life-cycle cost (ELCC) by 42.8% compared to optimal traditional single-layer deployment. Multi-layer staged deployment is more cost effective than single-layer staged deployment in all practical cases, which decreases ELCC by 22.9% compared to traditional deployment. Several cost altering mechanisms in staged deployment are identified. The results and analysis provide improved economic performance and better resource utilization, thus contributing in the long term to improved sustainability and market resilience. An accompanying decision support system provides system engineers with valuable insights on how to reduce deployment costs using the proposed multi-layered staged strategy. • Staged mega-satellite constellations deployment provides flexibility to deal with uncertain growing global internet demand. • A proposed framework enables more adaptable, sustainable, and resilient multi-layer staged deployment (MLSD) systems. • Results show 43.1% decrease in expected life-cycle cost (ELCC) compared to traditional design strategies. • Flexible MLSD is 22.9% more cost effective than flexible single-layer staged deployment. • A decision support system provides further insights on design and deployment of flexible MLSD systems. [ABSTRACT FROM AUTHOR]

Recent technological developments make perovskite solar cells (PSCs) particularly suitable for building integrated photovoltaic (BIPV) applications on vertical building envelopes, but the relatively short lifespan of PSCs requires frequent replacement, thereby generating substantial waste. Careful consideration of circularity credentials of this novel technology is therefore essential prior to the extensive implementation of vertical PSCs in building envelopes. This paper provides a circular economy approach for the implementation of PSCs in vertical envelopes and assesses its economic feasibility by life cycle cost analysis in Europe. The process of recycling PSC is developed. The economic performance of PSC envelopes is provided and compared to that of the conventional rigid BIPV system. Uncertainty analysis and sensitivity analysis of economic indicators are then performed to identify the influential parameters. The findings indicate that the PSC envelope has a significant potential for circular BIPV components and that PSCs applied on vertical envelope are economically viable. • LCCA of vertical PSC envelopes with recycling strategy is analyzed in Europe. • Recycling strategy of PSC is explored before diffusion to overcome PSC's short lifespan. • PSC price is the most influential factor for the deviation of LCOE. • PSC envelope has potential to achieve profitability in most regions of Europe. [ABSTRACT FROM AUTHOR]

Food systems are dominated by multiple risks inflicted by their surrounding environments that pose pressures on the continuous and efficient supply of food products. Market dynamics represented by competitive and cooperative behaviors of the system's main players is one of the uncertainties that influence the availability and affordability of food products. Efficient modeling of these risks can assist decision-makers in simulating and predicting future actions that mitigate the impact that market dynamics have on the provision of food products. This paper suggests an Agent-based model-based framework that simulates the behavior and tracks the economic and environmental performances of the tomato supply system in the State of Qatar. It considers three different scenarios representing the current situation of the market in addition to a competitive and a cooperative trade network. Interactions between trade partners are modeled using two game theoretic approaches counting Cournot competition and Cartel collusion. Based on the research findings, a competitive global market can bring economic benefits to Qatar as an importing nation, but this approach also entails substantial water consumption and results in significant environmental emissions. Conversely, collaboration among exporting countries demonstrates a potential 20% reduction in water usage and a 15% decrease in environmental emissions compared to the competitive scenario, offering enhanced environmental performance albeit at higher economic costs. This study provides a prescriptive analysis that enables policymakers to quantify the trade-offs between economic efficiency and environmental sustainability. These insights are crucial for guiding strategic planning for food supply security and environmental conservation. By understanding the implications of market structure changes, policymakers can navigate potential challenges more effectively and develop targeted policy interventions that enhance the resilience of the food supply system against market volatility and environmental impacts. This analysis supports proactive policy-shaping that aligns with their desired outcomes, ensuring a balanced approach to managing economic benefits and environmental considerations. [ABSTRACT FROM AUTHOR]

In this paper, we address the economic performance of Model Predictive Control (MPC) while operating at a backed-off operating point. Operating the plant at a constrained optimal point will often cause constraint violations due to uncertainties such as disturbances and measurement errors, etc. To ensure dynamic feasibility, the concept of economic back-off is used. In this work, we select the set point as the economic back-off point such that the dynamic operating region should have the least variability in the active constrained variables while ensuring the feasibility of other variables. In other words, the dynamic operating region is oriented by the proper design of a controller such that variability in active constrained variables is as low as possible. This controller design can be transformed into equivalent objective function weights of the MPC controller. In this study, we demonstrate that the determined back-off point is optimal for both linear controller and MPC controller when there are no unconstrained degrees of freedom. For the case with unconstrained degrees of freedom, the back-off point determined using the presented approach is optimal only for a linear controller but suboptimal for an MPC controller. Demonstrative case studies are presented to illustrate the economic performance of the MPC controller at the determined economic back-off point. • We proposed an economic back-off formulation for linear discrete-time systems. • We adopted a two-stage iterative solution strategy to determine optimal back-off. • We assess the economic performance of the MPC controller using inverse optimality. • We emphasize the role of unconstrained degrees of freedom in determining back-off. [ABSTRACT FROM AUTHOR]

The central environmental protection inspection is an institutional innovation in China that aims to supervise enterprises by monitoring government environmental enforcement. This paper examines the effectiveness of central environmental protection inspection in improving enterprise environmental performance by using a quasi-natural experiment of the first round of central environmental protection inspection. The study reveals that the central environmental protection inspection improves enterprise environmental performance by strengthening local government environmental enforcement and environmental attention. This is evidenced by the increase in enterprise environmental capital expenditure and the decrease in pollution discharge fees. The impact of central environmental protection inspection on enterprise environmental performance is strengthened by local governments' environmental regulation and environmental fiscal expenditure, as well as by public participation. Under the pressure of central environmental protection inspection, heavily polluting enterprises engage in production process improvement and R&D investment, which can improve their economic performance. This can ultimately lead to a competitive advantage for enterprises, promoting a win-win situation for both environmental and economic performance. [ABSTRACT FROM AUTHOR]

The manufacturing industry is facing a major challenge in balancing economic performance with environmental sustainability. To address this challenge, extant research has suggested that servitization could help align these objectives. However, the current evidence base is too scattered for policymakers to act on and support servitization in a targeted manner. Therefore, we conduct a systematic literature review to analyze over 120 research papers to establish the current understanding of the impact of servitization on the environmental and economic performance of a firm, and identify the contextual variables affecting this impact. The study identifies and critically appraises the body of literature that provides the current evidence base on the impact of servitization, the core areas of impact investigated and the methods that are used to establish this impact. Additionally, we conduct a thematic analysis of variables of impact to explore the theoretical perspectives that are used to explain the impact of servitization. Building on these theoretical perspectives we offer concrete propositions to further develop the research on the impact of servitization on environmental and economic performance. [ABSTRACT FROM AUTHOR]

The final geometry of open stopes often diverges from the design, creating unintentional volumes of overbreak and underbreak. Tools, such as the Stability Chart, are used for predicting overbreak, but its limitations regarding the precision of the predictions and the exclusion of underbreak have constrained the benefits of a predictive tool at the design stage. Furthermore, the economic aspects are not integrated when addressing geomechanical stope design. This paper presents how understanding and predicting stope performance at a meter-scale resolution through a multivariate model can be used for assessing the geotechnical (overbreak and underbreak) and economic stope performance. These predictions estimate the magnitude and location of overbreak and underbreak across the design surface, and are used to resolve the expected geometry of the mined void. The incorporation of a predicted mined void with grade values from a block model allows for a comprehensive economic stope performance estimate. This work is a significant step towards a stope design and planning process that minimizes the stability issues, dilution and loss of ore across the stope. As designing for higher ore recovery increases the potential for dilution and vice versa, this approach allows for the value realised from mining a stope to be maximised by quantifying this inherent economic trade-off. The enhanced resolution of both data and predictions has allowed for a more comprehensive evaluation of the predicted stope performance and economic outcomes during the design phase. This marks a notable advancement in the ability to design and plan the optimal stope. • A new probabilistic stope design approach for assessing the expected mining void. • Utilises data at a meter scale resolution and a multivariate model. • A comprehensive evaluation of the predicted overbreak and underbreak. • Incorporation of grade to quantify the economic trade-off of different designs. • It marks a notable advancement in designing and planning the optimal stope. [ABSTRACT FROM AUTHOR]

This paper compares the performance of conventional and organic fruit farms in Spain, using a set of base indicators to assess their economic and environmental performance on a per hectare basis. Composite indicators are also calculated to measure the overall economic and environmental performance of both production systems. Comparisons are made using propensity score matching to minimize the non-randomization biases caused by structural differences between the samples of conventional (n = 552) and organic (n = 127) fruit farms sourced from the Spanish Farm Accountancy Data Network (RECAN). The results based on per hectare metrics point to modest changes in the performance of farms that converted to organic farming. This is mainly because most converted farms were former conventional farms characterized by lower profitability and less intensive input use, consequently minimizing the effects of the conversion process. Nevertheless, the conversion outcomes exhibit a degree of variability depending on the specific type of fruit production. Economic gains are only discernible in the case of nut farms, whereas fruit and tropical fruit farms tend to yield the most favorable results per hectare from an environmental perspective. • Changes in per hectare performance are assessed when fruit farms convert to organic. • A complete set of economic and environmental base and composite indicators is used. • A matching procedure is implemented to obtain unbiased difference estimators. • Small changes are found in the per hectare performance of farms after conversion. • Mixed results depending on the type of fruit production subsector considered. [ABSTRACT FROM AUTHOR]

Real-time optimization with persistent parameter adaptation (ropa) is an rto approach, where the steady-state model parameters are updated dynamically using transient measurements. Consequently, we avoid waiting for a steady-state before triggering the optimization cycle, and the steady-state economic optimization can be scheduled at any desired rate. The steady-state wait has been recognized as a fundamental limitation of the traditional rto approach. In this paper, we implement ropa on an experimental rig that emulates a subsea oil well network. For comparison, we also implement traditional and dynamic rto. The experimental results confirm the in-silico findings that ropa 's performance is similar to dynamic rto 's performance with a much lower computational cost. Additionally, we present some guidelines for ropa 's practical implementation and a theoretical analysis of ropa 's convergence properties. [Display omitted] • rto with persistent parameter adaptation (ropa) is implemented on lab scale plant. • Steady-state and dynamic rto are also implemented for comparison. • ropa avoided ss detection and showed economic performance comparable to drto. • Guidelines for the practical implementation of ropa. • An analysis of ropa convergence to the steady-state optimum is carried out. [ABSTRACT FROM AUTHOR]

Abstract Many papers have been written on financial indicators to assess the use of a maintenance policy based on Total Productive Maintenance, while others have compared results showing the impact of criteria such as the Mean Time Between Failures. This paper provides the maintenance managers indicators which can assess the relevance of the actions carried out as well as readjustment of the planned maintenance program. In long term, this indicators knowledge may lead them to review their maintenance policy. To reach this aim, we propose several indicators for reliability, diagnosis and prognosis to assess and improve the maintenance policy based on Total Productive Maintenance. Methods used to obtain these indicators are only based on operating maintenance activities. These latter were extracted from a database produced by a Computerized Maintenance Management Information System. This work focuses on a maintained process based on total productive maintenance in which available sensor data are not indicative of degradation level achieved by the system. Indicators presented were obtained using Survival Laws, Hidden Markov Model and Support Vector Machine. As a concrete case study, an alloy foundry process is used. [ABSTRACT FROM AUTHOR]

Abstract Ecological civilization education is a critical, educational support for construction of ecological civilization. The structure chart of ecological education evaluation below shows that process evaluation plays a decisive role in determining the results of summary evaluation. The construction of an indicator system in this paper is just by focusing on the core content of ecological education –Educational PROCESS – so to incorporate the theory of ecological civilization into the whole college education. This paper is based on the modified AHP (Analytic Hierarchy Process) to establish a comprehensive evaluation model, construct judgment matrix and correct the weights, and to construct the framework and indicator system for evaluation of ecological civilization education in colleges and universities. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the Genuine Progress Indicator (GPI), which is a measure of sustainable economic welfare, for California for a five-year period, 2010–2014. This relatively short time period, which covers the recovery from a deep recession, allows us to examine how integration of environmental degradation, nonmarket activities, and inequality affects the GPI of California. The California GPI is only 52% of Gross State Product (GSP) – comparable to other GPI to GDP proportions – because the large negative environmental components offset the large positive social components and because many government expenditures, such as those related to defence and law enforcement, are excluded. Between 2010 and 2014, California GSP grew 9.2% and GPI grew 9.8%. We evaluate our estimation of the California GPI (CA-GPI) in two specific ways. First, we compare California’s GPI to an alternative indicator of social welfare, the Human Development Index (HDI) for California. Our comparison points out that the GPI is a more holistic measure of sustainable economic well-being, although the HDI is useful for evaluating educational attainment, life expectancy, or earnings across regions or demographic groups in the state. Second, we compare our estimation of CA-GPI to the California results from a recent GPI estimation for all fifty states for 2011, in order to evaluate how different methodological decisions and data selection affect the results. Our overall estimate is 13 percent higher, with the primary differences reflecting discrepancies in methodological assumptions or data sources in the calculation of a few key variables, including the value of time used for calculating nonmarket activities. These two estimates of CA-GPI allow us to analyze the sensitivity of two widely used approaches for calculating the GPI, and the sensitivity of using California-specific public data sources compared to national public data sources (scaled to California). The variation in the two California GPI estimations demonstrates the importance of standardizing the method and the data sources, with the goal of creating a viable alternative to the GDP for measuring economic performance. Comparison of the two GPI estimations shows how the use of region-specific data increases the accuracy of estimates, which is important for evaluating regional outcomes and trends over time. However, using data and method that prioritizes standardization is essential for cross-regional comparability, even though the trade-off is diminished regional accuracy. The paper concludes with a discussion of the uses of the GPI to evaluate policies, and suggests fruitful steps forward. [ABSTRACT FROM AUTHOR]

This paper looks for empirical support to the existence of a positive nexus between economic and environmental performance in farming as implied by the Sustainable Intensification hypothesis. As the ecological scale at which this nexus actually occurs is unobservable, the paper juxtaposes its estimation at three spatial scales, the farm level and two regional levels. Starting with a common theoretical background, the paper estimates a dynamic spatial panel model on these alternative scales. Identification issues (granularity, aggregation bias and spatial dependence) may generate significantly discrepant estimates eventually questioning the reliability of these results. The empirical study investigates the relationship between total factor productivity, greenhouse gas emissions and crop diversity using a 2008–2018 panel of Italian farms. Results show that the productivity-environment nexus changes and may even revert its sign when passing from farm-level to aggregate data. The implications of these results for evidence-based policy making are discussed. • Existence of a positive productivity-environment nexus in farming is investigated. • From a joint-production framework a Dynamic Spatial Panel Model is derived. • The empirical assessment critically depends on the spatial scale of analysis. • On the basis of the Italian FADN three farm and regional scales are considered. • Results reveal that different spatial scales may provide non-concordant evidence. [ABSTRACT FROM AUTHOR]

There has been widespread debate about whether the way in which we measure economic activity is fit for purpose in the twenty-first century. One aspect of this debate is to move away from measuring a nation's income (GDP) towards monitoring a nation's assets (their inclusive wealth), as a better indicator of sustainable economic development. We provide the first critical comparison of the approaches of leading international organisations – the World Bank and the United Nations Environment Programme (UNEP) – to estimating changes in wealth. Our paper reveals important inconsistencies in how these organisations measure sustainability and the conflicting messages that policy makers receive, despite a common underlying conceptual framework linking changes in a nation's wealth to future well-being. We attribute these differences to methodological (applied theory) choices made by researchers at the respective institutions. These choices matter. At the most extreme, countries that perform the worst according to the UNEP are shown to perform well according to the World Bank. This confusion in signals makes better policy making more difficult. [ABSTRACT FROM AUTHOR]

Solar forecasting is a rapidly evolving field that can substantially contribute to the effective integration of large amounts of solar photovoltaic (PV) capacity into the electricity system. However, newly developed solar forecasting models are rarely tested in an operational context considering the intended application and objective. Besides, models are typically evaluated considering only technical error metrics, disregarding their economic value. This paper proposes an operational bidding strategy that optimizes the participation of a PV power plant in the electricity spot markets. To this end, a novel multistage stochastic optimization method is developed that considers the day-ahead, intraday, and imbalance markets. As the developed method utilizes a scenario generation algorithm, the proposed method can be adopted for a wide variety of related applications. The performance of the developed method is assessed using technical and economic metrics and compared to a reference method. The results demonstrate the effectiveness of the proposed bidding strategy, as it substantially outperforms the reference market bidding strategy. The findings also provide insights into the value of a multistage bidding method, as extending market participation from the day-ahead to the intraday market increases revenues by 22%, while halving the total imbalance. Additionally, the study examines the relationship between the technical and economic performance of solar power forecasting models, revealing a non-linear correlation. • We develop an operational bidding strategy for PV plant operators participating in electricity spot markets. • The strategy concerns a multistage scenario-based stochastic method. • Time-independent probabilistic solar power forecasts are transformed into interdependent scenarios. • The results are thoroughly evaluated, considering technical and economic metrics. • The application of the method leads to increased revenues and reduced imbalances. [ABSTRACT FROM AUTHOR]

Water security is widely recognised as an important and increasingly urgent policy challenge. To address this challenge, appropriate indicators are needed to stimulate policy action and measure the effectiveness of interventions. Indicators are more likely to have an impact on policy formulation when they are valid, credible and salient. This paper applies a co-production approach to develop a set of Urban Water Security Indicators which aim to meet these criteria. The approach was piloted in two cities, Singapore and Hong Kong. Data for a 15-year period were collected and stakeholder consultation and validation conducted. The paper reports on the indicator development process and findings, and reflects on the value of the Urban Water Security Indicators as a tool in policy formulation. The approach is being replicated in a larger group of cities as part of an ongoing research programme. [ABSTRACT FROM AUTHOR]

This paper presents a techno-economic model based on experimental and market data able to evaluate the profitability of vanadium flow batteries, which are emerging as a promising technology for specific stationary energy services. Models like this are very informative on the present and perspective competitivity of industrial flow batteries in operating specific services, but they have not yet been developed to an accurate grade. This model uses technical parameters which are taken from large-area multi-cell stacks, rather than from small single cell experiments, to better characterize the behavior of real industrial reactors, and from real financial market and economic patterns of some major manufacturers. The model yields economic performance indicators as the capital cost, the operative cost, the levelized cost of storage and the net present value. A prudential present-state and a perspective analysis are elaborated to show where the economic indicators are heading, and which parameters affect more the investment profitability, thus tracking a possible roadmap for system optimization. Perspective estimations indicate that technological and market evolutions are heading to much more competitive systems, with capital costs down to 260 € kWh−1 at a energy/power duration of 10 h, to be compared with a break-even point in the net present value of 400 € kWh−1, which suggests that flow batteries may play a major role in some expanding markets, notably the long duration energy storage. [Display omitted] • A techno-economic model for vanadium redox flow battery is presented. • The method uses experimental data from a kW-kWh-class pilot plant. • A market analysis is developed to determine economic parameters. • Capital cost and profitability of different battery sizes are assessed. • The results of prudential and perspective analyses are presented. [ABSTRACT FROM AUTHOR]

This paper takes a look at the impact of eco-control systems on the circular economy (CE) practices and economic and environmental performances of firms, taking in account the moderating role that environmental uncertainty can play in this regard. Based on data from a sample of 205 French manufacturing firms, and using hierarchical regressions analysis, the study reveals that eco-control systems improve CE practices as along with both ecological and economic performances. As such, this study provides novel insights challenging thus extant literature which argued that the influence of ecological control systems on economic performance may not be direct and may come about through specific organizational actions or initiatives. Research also reveals that the impact of eco-control systems as mediated by CE practices differs depending on the nature of the company's performance. More precisely, eco-control systems improve environmental performance through CE practices while no indirect effect was found with regard to economic performance. In doing so, this study provides novel insights to existing works by highlighting the positive role that CE plays in value creation of businesses. Moreover, environmental uncertainty was found to be a main contextual factor that positively moderates connection between ecological control systems and economic performance. As such, this study provides novel insights and broadens the literature in this area. This work generally offers an innovative view of the role that ecological control systems play in managing firms' CE practices to achieve better performance under environmental uncertainty conditions. [ABSTRACT FROM AUTHOR]

With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience. • A multi-objective integrated energy systems interaction model is established. • The interaction model considers multiple energy types and uncertainties. • The model can improve economy and flexibility of each system agent and the region. [ABSTRACT FROM AUTHOR]

Biophysical Economics is a school of thought in heterodox economics built on the premise of the primacy of energy in the economic process. Despite significant progress made in the methodology of net-energy analysis, the literature on the relationships (if any) between the biophysical properties of energy sources, such as net-energy ratios, and financial indicators (price, cost, etc.) is scant. Are the biophysical qualities of energy sources reflected by market signals? As such, can the latter guide decision-making in the context of the ongoing depletion of non-renewable energy resources? The paper examines the relationships between the net-energy ratios and price, cost of production and price-to-cost ratios of the Canadian oil sands produced via open-pit mining from 1997 to 2016. A simple econometric model is developed to estimate the correlation between the standard Energy Return on Energy Invested (EROIst) with the price, cost of production and price-to-cost ratio of diluted bitumen and synthetic crude over a 20-year period. Preliminary results suggest the absence of correlation between any pair of biophysical and financial variables. No discernable correlation is identified between the EROIst and financial indicators of either crude stream, suggesting biophysical and financial properties to be sui generis realities. [ABSTRACT FROM AUTHOR]

Based on the panel data of Changjiang Economic Corridor (CEC), this paper includes agricultural carbon emissions as an unwanted output into the evaluation index system for agricultural carbon emissions performance (ACEP), and measured the ACEP of the 11 provinces in the CEC of 2005–2018, using the minimum distance to weak efficient frontier (MinDW) model. In addition, a Tobit model was constructed for empirical analysis on the impactors of CEC ACEP. The results show that: In general, the CEC achieved an excellent ACEP, with certain differences between the provinces; the ACEP trends varied between the upper, middle, and lower reaches of the CEC, and the lower reaches had a much higher ACEP than the upper and middle reaches; the results of Tobit model demonstrate that industrial upgrading and rural power consumption have significant positive correlations with CEC ACEP, while human capital, planting structure, and agricultural disaster have significantly negative correlations with CEC ACEP. [ABSTRACT FROM AUTHOR]

Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers’ outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012. [ABSTRACT FROM AUTHOR]

This paper assesses the feasibility of determining key household characteristics based on temporal load profiles of household electricity demand. It is known that household characteristics, behaviours and routines drive a number of features of household electricity loads in ways which are currently not fully understood. The roll out of domestic smart meters in the UK and elsewhere could enable better understanding through the collection of high temporal resolution electricity monitoring data at the household level. Such data affords tremendous potential to invert the established relationship between household characteristics and temporal load profiles. Rather than use household characteristics as a predictor of loads, observed electricity load profiles, or indicators based on them, could instead be used to impute household characteristics. These micro level imputed characteristics could then be aggregated at the small area level to produce ‘census-like’ small area indicators. This work briefly reviews the nature of current and future census taking in the UK before outlining the household characteristics that are to be found in the UK census and which are also known to influence electricity load profiles. It then presents descriptive analysis of a large scale smart meter-like dataset of half-hourly domestic electricity consumption before reviewing the correlation between household attributes and electricity load profiles. The paper then reports the results of multilevel model-based analysis of these relationships. The work concludes that a number of household characteristics of the kind to be found in UK census-derived small area statistics may be predicted from particular load profile indicators. A discussion of the steps required to test and validate this approach and the wider implications for census taking is also provided. [ABSTRACT FROM AUTHOR]

• Determination of the impact of the perishability of products on the agri-food supply chain design. • Integration of farming to distribution tactical decisions on the agri-food supply chain design. • Perishability of products impact on the design of agri-food supply chains commercializing short shelf-life products. • The same supply chain configuration is obtained for agri-food supply chains commercializing long shelf-life products. Perishability of agri-food products impacts the economic, environmental, and social aspects of agri-food supply chains (AFSCs). Product perishability is, usually, considered in tactical and operational decisions, but not in strategic ones, such as the design of the AFSC. The contribution of this paper is that it investigates the impact of product perishability on an AFSC design. To do so, first, a novel mixed-integer linear programming model is proposed to design entire AFSCs with multiple-products, which considers capacity, planting, harvesting, transporting and perishability constraints for a multiple-period horizon. A set of scenarios is generated by varying products' shelf life and analysed. The results show that product perishability is relevant when designing AFSCs, especially for products with a short shelf life. The results show that an AFSC's economic performance improves when product perishability is considered. The model can also help in determining the investment needed extend products shelf life while remaining profitable. Other uses could include tactical planning for chains already in place. [ABSTRACT FROM AUTHOR]

This paper proposed a double-layer model predictive control (DLMPC) strategy integrated with zone control. In the steady-state target calculation (SSTC) layer, the controlled output variables are pretreated based on the analysis of process principle and production data. Subsequently, the optimal input–output targets and corresponding zone parameters are obtained by solving the steady-state optimization problem and transmitted to the dynamic control layer for tracking. Meanwhile, the weighted and priority soft constraints relaxation methods are discussed when the SSTC problem is infeasible. Compared with conventional DLMPC and zone model predictive control (ZMPC), the proposed algorithm can not only achieve smooth control, but also have better robustness and economic performance. Finally, a simulation example is taken to show the effectiveness of the proposed method. • More robust control algorithm for uncertain complex industrial systems. • Effective methods are described to solve the infeasible optimization problem. • The zone parameters are adjusted in real-time according to the states feedback. • Both economic and robust performance are considered. [ABSTRACT FROM AUTHOR]

This article aims to highlight various methods and approaches to grouping countries, according to the behavior of their open innovation indicators. GDP, inflation and unemployment are the most important indicators of the economic and social policies of states, allowing them to be evaluated and models built. To find the relationships between open innovation indicators the paper uses marginal analysis and feature reduction, as well as machine learning methods (shift to the mean, agglomerative clustering and random forest methods). The results showed that, after isolating all groups, the importance of the signs was established and the patterns of behavior of indicators for each group were compared and open innovation dynamics was analyzed. The conclusions showed that it is obvious that increasing the number of variables in the model and using more extensive indicators can greatly increase the accuracy, in contrast to the generally accepted simple classifications. This approach makes it possible to more accurately find the connections between sectors of the economy or between state economies in general. An accompanying result of the study was the clarification of the equality of open innovation indicators for the analysis of their interrelationships between countries. [ABSTRACT FROM AUTHOR]

As manufacturing processes transition towards digitalization, data-driven process control is emerging as a key area of interest in future artificial intelligence technology. A crucial aspect in implementing data-driven process control is "What should we learn from the data?". In general, the data-driven control method can be categorized into two main approaches: Learning the model and learning the value. To assist in selecting the more suitable approach, this paper applies six different control methods, with three falling under each approach, to three distinct manufacturing process systems. The simulation results indicate that the model-learning approaches display higher data efficiency and exhibit lower variance in total cost. These methods prove to be particularly advantageous for addressing the regulation problems. Conversely, value-learning approaches show competitive potential in closed-loop identification and in managing economic cost problems. The remaining challenges associated with each technique are discussed, along with practical considerations for their implementation. • What should we learn, model or controller, with the collected data? • Quantitatively compare the model-based and model-free control approaches. • Model-based methods were superior in tackling regulation problems. • Model-free methods show competitive performance in optimizing economic cost. [ABSTRACT FROM AUTHOR]

Sharing economy platforms hold a promise of more sustainable uses of resources. However, their legitimacy remains "debated" as various ecosystem stakeholders hold different opinions, challenging platform's values and activities. One key contested aspect is their capacity to contribute to sustainability beyond a search for economic performance. Despite these criticisms, sharing economy platforms have managed to grow and spread worldwide. Nevertheless, little is known about how a sudden market crisis affects an online platform characterized by a debated legitimacy. Studying the relationships between Airbnb and its main stakeholders during the first wave of COVID-19 pandemic in New York City, our paper identifies three relational strategies - identity, professionalization, and selection - implemented by the platform to reinforce its legitimacy within its local ecosystem. Based on secondary and primary data analyses, we identify and describe the arbitrations and opportunistic choices made by Airbnb to manage various stakeholder groups, to maintain its legitimacy during the COVID-19 crisis, and their impact on sustainability. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of business regulations, policies and institutions on global value chains (GVCs) in Sub-Saharan Africa (SSA). It uses the latest release of the Eora MRIO I O database to compute the GVC indictors. The World Bank's doing business indicators are used as business regulatory measures and the Country Policy and Institutional Assessment (CPIA) indicators are also used as policies and institutional quality measures. The empirical evidence is based panel data analyses and panel quantile regressions as the estimation techniques. The findings show that business regulations, institutional and policy initiatives are critical factors in determining the level of GVC participation in Africa. The results also provide evidence that Africa countries could enhance their participation in the global value chain through improved business regulations, policies and institutions. • We examine impact of business regulations, policies and institutions on global value chains in Sub-Saharan Africa. • We use the latest release of the Eora MRIO I O database to compute the GVC indicators. • We employ multiple panel data analyses and panel quantile regressions as estimation techniques. • We find business regulations and institutional and policy initiatives to be critical factors determining GVC participation. [ABSTRACT FROM AUTHOR]

Different types of technical barriers in the operation of an eco-industrial system have been identified theoretically as a result of the exploration and implementation of ecological industrial parks since the 1990s. However, the vulnerability of these systems is amplified by inadequate management. In this paper, we describe the indicators of stability in an eco-industrial system based on complex network theory to build a cascading failure model for the system. A case from the Qinghai Salt Lake Ecological Industrial Park is introduced to construct the network topological structure and simulate the network cascading failure transmission. We discuss how two types of node enterprises impact the stability of the eco-industrial system. The results show how various node enterprises with different mechanisms affect the systematic stability of the eco-industrial network and how the removal of core node enterprises can lead to greater damage to the network. Managers of eco-industrial systems should focus on structural core enterprises and core industrial chains. In this paper, the managerial implications can provide reliable guidance for stable operations in an eco-industrial system. [ABSTRACT FROM AUTHOR]

Despite several decades of academic and practical debate on tourism sustainability, its application in practice remains difficult. The dominant tourism discourse on sustainability (theory, seen as a concept) and responsibility (practice, understood as appropriate action) calls for a solid understanding of the process of how a responsible destination actually implements a sustainability agenda, which this paper aims to provide. In this context, we explore theoretical perspectives from political economics and behavioural economics to offer a well-reasoned integrated sustainability–responsibility model comprising three stages: Awareness, Agenda and Action. This Triple-A Model complements the sustainability indicators debate and provides advice on how to continuously implement the sustainability concept and move from market-value-led and environmentally laissez-faire tourism towards more environmental- and social-value-driven responsible tourism. In addition, this paper discusses the existing sustainability and responsibility nomenclatures and their use and contributes relevant conclusions on the current understanding of sustainability and responsibility in European and UNWTO practices. The term responsustable tourism is suggested to join two existing terms and demonstrate that the current understanding of responsible tourism behaviour is based on the concept of sustainable tourism. [ABSTRACT FROM AUTHOR]

As any science on progress, there is no serious claim of mastering a process that is not carrying its own legitimized means of being measured. TRIZ world is now becoming a stable and progressing community where contributions of various levels, nature and depth will undoubtedly continue to appear. How can we contribute to its performance, how can we detect what is lacking and investigate in TRIZ world, new areas of contribution? This paper proposes an investigation of the existing means in creative measurement applied to industry and draw a state of the art and discussion regarding its limitations. Then the paper reports about a survey we built to understand R&D director's expectations regarding an eventual indicator. This survey yet concern a narrow spectrum of the entire industry because we first wanted to analyze results out of a smaller sample and either by extension or with larger survey compare industries, then complete and fine tune our first conclusions. [ABSTRACT FROM AUTHOR]

This paper addresses the use of impact indicators with respect to climate change in the 2007–2013 Rural Development Programme (RDP) of the European Union, with particular reference to the Scotland Rural Development Programme (SRDP). It concludes that the policy context has highlighted the need for the rural land use sector to respond to climate change but that the associated Common Monitoring and Evaluation Framework (CMEF) did not develop suitable indicators to assess the impact of SDRP measures on GHG emission mitigation. It suggests improved impact indicators based on the relationship between rural land use and greenhouse gas (GHG) emissions: first, an indicator based on net GHG emissions per holding; and a second based on net GHG emissions per unit volume of output. The paper points out the challenges in measuring land-based emissions accurately. It further proposes screening of RDP measures to ensure that climate change mitigation impacts are properly appraised. It is recognised that climate change policy in relation to rural land use is still at an early stage of development but greater sophistication of policy instrument design and evaluation will be required if the RDP is to contribute significantly to climate change policy objectives. [ABSTRACT FROM AUTHOR]

Mixed land-use is one of the raised issues that still constitutes a controversial matter for many researchers, whether those advocating the use of it or opposing the idea of its existence. Hence, this research discusses the concept of mixed land-use and the positives and negatives (Pros & Cons) that can be realized regarding one of the new conceptions, represented in social capital. This concept is considered relatively recent and one of the most important resources for the achievement of sustainable development. This research paper aims at considering the influence of mixed land-use on realizing the social capital via studding the mutual relationship between the two variables in more than one zone in the Greater Cairo Region. This research paper handles the topic in terms of three themes. The first theme handles the mixed land-use in terms of the concept, historical development, positives and negatives and reaching to its measurable variables. Whereas the second theme deals with the social capital in terms of its concepts, forms, types and its measurable indices. And the third theme deals with the selection of six study areas in the Greater Cairo Region and the measurement of each index of mixed land-use and social capital in these zones for reaching to the mutual relationship between the two variables. Whereas the search has proved the presence of a direct relationship between them; the more the ratio of mixed land-use, the more the social capital. [ABSTRACT FROM AUTHOR]

This paper addresses the pressing research gap that demands an overarching framework to assess the progress of mining, minerals and energy supply and demand networks towards sustainable development. The framework proposes a hierarchy from Sustainable Development, which begins at the global scale, to Operating Sustainably, which begins at the level of unit operations. The two concepts overlap in the middle with a focus upon regional economies. Guidance is provided to assess the performance of both dynamic dimensions: Sustainable Development is assessed in terms of equity and Operating Sustainably is assessed in terms of the changing states of maturity of the activities being monitored. The papers from this Special Volume were the input sample to assess the extent to which these performance dimensions were being addressed. In analysing the progress towards Sustainable Development, it was found that papers were mainly focussed on equity within supply countries, but they provided little attention to the other three equity conditions proposed: demand met at a reasonable price; future needs met; and profitable transfer agents. The transition towards Operating Sustainably was promising; with results that revealed progress along the proposed four-stage maturity journey which includes: (1) Solely profit maximising; (2) Efficiency Focussed; (3) Integrating by Connecting and (4) Adaptable & Resilient. A comprehensive definition for Operating Sustainably is proposed for discussion and refinement. It is suggested that the definition proposed can be used for selecting appropriate indicators for measuring and documenting progress. [ABSTRACT FROM AUTHOR]

This paper describes the life cycle assessment and life cycle cost analysis of a typical Australian house designs. It evaluates the effect of selected alternative wall assemblages on environmental impacts and life cycle cost over the various life stages of buildings (i.e. construction, operations, maintenance and final disposal). A case study house was used as the base case for all the alternative wall assemblage designs. This paper also reports on alternative wall assemblage designs that were produced with variations in external wall cladding, insulation type and thickness, air gap thickness and position. Each design was varied such that it achieved a chosen star rating. Five exterior wall claddings were selected, typical of the Australian building industry. These claddings were brick, autoclave aerated concrete block, fibro-cement sheet, pine saw logs and weatherboard. The results were analyzed for the whole building on a whole life cycle basis in terms of economic and environmental impact. The implications of life cycle environmental impacts and life cycle costs were evaluated and the optimum assemblage design is reported using optimization algorithm. A set of best solution is found depending on factors: the model assumptions, range of environmental and economic indicators considered, and the chosen quantitative criteria. [ABSTRACT FROM AUTHOR]

Economic performance is one of the important things that have to be evaluated by the government. There are many factors that affect economic performance. This paper aims to evaluate the effect of fiscal decentralization indicators on regional economic performance in Sumatra Island, Indonesia. Based on the results of spatial dependency testing, can be concluded that there are spatial dependencies on economic performance and spatial heterogeneity between locations in Sumatra Island. Therefore, Geographically Weighted Regression (GWR) which is a spatial modelling can be used in this case. There are six independent variables that be used in this research, there are original local revenue, general allocation fund, special allocation fund, and profit share fund, poverty rate and human development index. The result showed that Geographically Weighted Regression model performed better than multiple linear regression of Ordinary Least Square (OLS) model, providing different effect information at each location. Based on model evaluation using RMSE, MAPE and AIC showed that GWR model is better than OLS model. [ABSTRACT FROM AUTHOR]

Credit risk rating can be described by several economic activity indicators. Utilizing these financial movement markers to build a tenable credit scoring model will enormously improve the precision of the model. This method can be used in a series of credit evaluations and specific economic conditions. A reasonable scenario in which the uncertainty is consistent. In this paper, the logistic regression algorithm is joined with weighted evidence to fabricate another credit score model. Through the relationship existing in economic activities, the connection of each economic movement is additionally dissected by utilizing the correlation orthogonal transformation in the weight of proof to improve the exactness of the model. In practice, due to numerous weaknesses in the records, there is significant error in the logistic regression. Hence, building of hybrid scoring model can increase the accurateness of credit score. Thus improved the prediction rate of user credit scores and reducing the occurrence of credit fraud. [ABSTRACT FROM AUTHOR]

Cities today are dense networks of interchanging investments, information, goods and people, as well as centers of innovation and knowledge management. In such a complex framework of housing, social and economic interrelationships, cities become large consumers of resources and producers of greenhouse gases. Therefore, and in order to preserve resources and guarantee social services and the well-being of citizens, planning and policy actions are required that contribute to achieving sustainable growth. Beyond the environmental perspective, a socio-economic analysis is essential to make a comprehensive sustainability diagnosis of urban and rural systems. This paper presents a methodology for sustainability assessment, based on 38 indicators that include the three pillars of sustainability: social, economic and environmental. To carry out this assessment, 64 municipalities out of 313 located in Galicia (Northwest Spain) were selected and classified in three categories according to their population size (Medium Size, Small Size and Village). Moreover, two weighting methods have been considered (equal weighting and measured weighting attributed through Analytic Hierarchy Process method). The results show that most sustainable municipalities are located in the north of the region. On the other hand, regardless of the weighting method, 57% of the medium-sized municipalities are rated sustainable compared to 45% of those of the categories of Village and Small Size categories. Therefore, municipal size is relevant for measuring sustainability and there are no significant differences between the results obtained with the two weighting methodologies, indicating that the method developed is robust and could be applied to other municipalities and cities. Image 1 [ABSTRACT FROM AUTHOR]

• Yield gap of 24–42%, profit gap of 36–82% and gaps in other indicators exists in six Asian countries. • In Vietnam, >80% of pesticide application practices are unsustainable. • Decrease fertilizer rates in Guangdong and Yogyakarta and increase in Bago, and increases labor productivity in Yogyakarta. • A greenhouse gas reduction potential of 25–35% exists across the six Asian countries. • SRP indicators could make rice production systems profitable with reduced environmental footprint. Quantifying and comparing sustainability indicators are essential to improve the sustainability of smallholder rice cropping systems. The sustainability of rice production systems can be measured based on economic, environmental, social, and institutional indicators. In this paper, we restrict our assessment to economic and environmental indicators. During 2012–2015, farmers were interviewed from 847 households from intensively irrigated rice production regions in Vietnam, Thailand, Indonesia, Myanmar, Sri Lanka, and China. We assessed the sustainability of their farming practices using economic and environmental indicators, i.e., eight of the 12 performance indicators (PIs), as defined by the Sustainable Rice Platform (SRP). Across the six sites, there was a yield gap of 24–42% and a profit gap of 36–82% between the 10% highest-performing farms (mean of top decile) and the mean-performing farms. In addition, there was a labor productivity gap of 12–32%, a nitrogen use efficiency (NUE) gap of 11–20%, a phosphorus use efficiency (PUE) gap of 1–29%, and a water productivity gap of 12–42%. Deliberate modification of conventional practices, including not flooding the field for >30 days before rice planting, incorporating pre-rice crop residue >30 days before planting, and adoption of mid-season drainage or alternate wetting and drying irrigation rather than continuous flood irrigation during the rice growing period, could substantially reduce the greenhouse gas (GHG) emission in irrigated rice fields without yield penalty. There is an urgent need to adopt improved management strategies for nitrogen (N), phosphorus (P), potassium (K) fertilizer, irrigation water-use efficiency, as well as for decreasing pesticide use frequency, without sacrificing profitability and yield. We identified the following priority interventions for each site: a) increasing fertilizer use and adopting higher-yielding varieties in Bago, Myanmar; b) reducing pesticide application rates in Can Tho, Vietnam; c) reducing fertilizer use in Guangdong, China; d) reducing nitrogen and labor use in Yogyakarta, Indonesia; e) reducing fertilizer and water use in Polonnaruwa, Sri Lanka and; f) reducing fertilizer use in Nakhon Sawan, Thailand. Additional uses of the PI analysis clearly demonstrated that rice yield and profit gaps can sustainably be closed by increasing efficiencies that will also lead to reduced environmental footprint. [ABSTRACT FROM AUTHOR]