Navigating the community renewable energy landscape:An analytics-driven policy formulation

In an era where climate change and energy security have become paramount concerns, community renewable energy (CRE) projects have emerged as an essential tool for engaging citizens in the transition to sustainable energy sources. Despite growing interest in CRE, limited research has been conducted to statistically understand the non-economic social factors that along with the economic and technical factors influence adoption and investment in such initiatives. Addressing this knowledge gap, our study presents a data-driven approach to examining the demographic, attitudinal, and heterogeneous socio-behavioural drivers in decisions to participate in CRE, with the aim of designing evidence-based local energy policies. In our study, we leverage insights from a large-scale survey of 941 Australians, which investigated some possible non-economic and economic factors and employ unsupervised machine learning techniques. We introduce the Stratified Harmonic Clustering Framework (SHCF), a comprehensive analytical approach that examines five clustering classes across nine distinct methods, completing 235,420 hyperparameter tuning iterations to determine the optimal algorithm for identifying distinct groups. Here, we present our novel Adaptive Nested DBSCAN algorithm, which reveals three distinct clusters with varying priorities, motivations, and attitudes towards renewable energy (RE): a) Senior CRE Enthusiasts, b) Urban RE Adopters and Advocates, and c) Rural RE Investors and Sceptics. Our findings suggest that i) Tailoring outreach efforts to these different demographic clusters, ii) Prioritising community needs and concerns, iii) Fostering positive attitudes and trust, iv) Implementing supportive regulations, and v) Devising economic incentives, are all crucial for promoting CRE adoption. Based on these insights, we propose targeted CRE policies for each identified cluster, underscoring the importance of addressing the unique priorities and motivations of these various groups. The key benefit of this approach is the potential to address debates surrounding the changes in social formations arising from energy transition, and the opportunities they present for increased resilience.</p

Navigating the community renewable energy landscape: An analytics-driven policy formulation

In an era where climate change and energy security have become paramount concerns, community renewable energy (CRE) projects have emerged as an essential tool for engaging citizens in the transition to sustainable energy sources. Despite growing interest in CRE, limited research has been conducted to statistically understand the non-economic social factors that along with the economic and technical factors influence adoption and investment in such initiatives. Addressing this knowledge gap, our study presents a data-driven approach to examining the demographic, attitudinal, and heterogeneous socio-behavioural drivers in decisions to participate in CRE, with the aim of designing evidence-based local energy policies. In our study, we leverage insights from a large-scale survey of 941 Australians, which investigated some possible non-economic and economic factors and employ unsupervised machine learning techniques. We introduce the Stratified Harmonic Clustering Framework (SHCF), a comprehensive analytical approach that examines five clustering classes across nine distinct methods, completing 235,420 hyperparameter tuning iterations to determine the optimal algorithm for identifying distinct groups. Here, we present our novel Adaptive Nested DBSCAN algorithm, which reveals three distinct clusters with varying priorities, motivations, and attitudes towards renewable energy (RE): a) Senior CRE Enthusiasts, b) Urban RE Adopters and Advocates, and c) Rural RE Investors and Sceptics. Our findings suggest that i) Tailoring outreach efforts to these different demographic clusters, ii) Prioritising community needs and concerns, iii) Fostering positive attitudes and trust, iv) Implementing supportive regulations, and v) Devising economic incentives, are all crucial for promoting CRE adoption. Based on these insights, we propose targeted CRE policies for each identified cluster, underscoring the importance of addressing the unique priorities and motivations of these various groups. The key benefit of this approach is the potential to address debates surrounding the changes in social formations arising from energy transition, and the opportunities they present for increased resilience

Extending the supply chain to address sustainability

© 2019 Elsevier Ltd In today's growing economy, overconsumption and overproduction have accelerated environmental deterioration worldwide. Consumers, through unsustainable consumption patterns, and producers, through production based on traditional resource depleting practices, have contributed significantly to the socio-environmental problems. Consumers and producers are linked by supply chains, and as sustainability became seen as a way to reverse socio-environmental degradation, it has also started to be introduced in research on supply chains. We look at the evolution of research on sustainable supply chains and show that it is still largely focused on the processes and networks that take place between the producer and the consumer, hardly taking into account consumer behavior and its influence on the performance of the producer and the supply chain itself. We conclude that we cannot be talking about sustainability, without extending the supply chains to account for consumers' behavior and their influence on the overall system performance. A conceptual framework is proposed to explain how supply chains can become sustainable and improve their economic and socio-environmental performance by motivating consumer behavior toward green consumption patterns, which, in turn, motivate producers and suppliers to change their operations

Sustainable Supply Chain Analytics: Grand Challenges and Future Opportunities

Over the last few years, the pressure for decreasing environmental and social footprints has motivated supply chain organizations to significantly progress sustainability initiatives. Since supply chains have implemented sustainability strategies, the volume of economic, environmental and social data has rapidly increased. Dealing with this data, business analytics has already shown its capability for improving supply chain monetary performance. However, there is limited knowledge about how business analytics can be best leveraged to grow social, environmental and financial performance simultaneously. Therefore, in reviewing the literature around sustainable supply chain, this research seeks to further illuminate the role business analytics plays in addressing this issue. A literature survey methodology is outlined, scrutinizing key papers published between 2012 and 2016 in the research fields of Information/Computing Science, Business and Supply Chain Management. From examination of 311 journal papers, 39 were selected as meeting defined criteria for further categorization into three distinct research groups including: (a) sustainable supply chain configuration; (b) sustainable supply chain implementation; (c) sustainable supply chain evaluation. The issues involved within each grouping are identified and the business analytics processes (i.e. prescriptive, predictive, prescriptive analytics) to specifically address them are discussed. This wide-ranging review of sustainable supply chain analytics can assist both scholars and practitioners to better appreciate the current grand challenges and future research opportunities posed by this area

Designing a conceptual framework for strategic selection of Bushfire mitigation approaches

Fires are an important aspect of environmental ecology; however, they are also one of the most widespread destructive forces impacting natural ecosystems as well as property, human health, water and other resources. Urban sprawl is driving the construction of new homes and facilities into fire-vulnerable areas. This growth, combined with a warmer climate, is likely to make the consequences of wildfires more severe. To reduce wildfires and associated risks, a variety of hazard reduction practices are implemented, such as prescribed burning (PB) and mechanical fuel load reduction (MFLR). PB can reduce forest fuel load; however, it has adverse effects on air quality and human health, and should not be applied close to residential areas due to risks of fire escape. On the other hand, MFLR releases less greenhouse gasses and does not impose risks to residential areas. However, it is more expensive to implement. We suggest that environmental, economic and social costs of various mitigation tools should be taken into account when choosing the most appropriate fire mitigation approach and propose a conceptual framework, which can do it. We show that applying GIS methods and life cycle assessment we can produce a more reasonable comparison that can, for example, include the benefits that can be generated by using collected biomass for bioenergy or in timber industries. This framework can assist decision makers to find the optimal combinations of hazard reduction practices for various specific conditions and locations

Explainable artificial intelligence in disaster risk management: Achievements and prospective futures

Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds the potential to enhance DRM through improved decision-making processes, its inherent complexity and "black box" nature have led to a growing demand for Explainable AI (XAI) techniques. These techniques facilitate the interpretation and understanding of decisions made by AI models, promoting transparency and trust. However, the current state of XAI applications in DRM, their achievements, and the challenges they face remain underexplored. In this systematic literature review, we delve into the burgeoning domain of XAI-DRM, extracting 195 publications from the Scopus and ISI Web of Knowledge databases, and selecting 68 for detailed analysis based on predefined exclusion criteria. Our study addresses pertinent research questions, identifies various hazard and disaster types, risk components, and AI and XAI methods, uncovers the inherent challenges and limitations of these approaches, and provides synthesized insights to enhance their explainability and effectiveness in disaster decision-making. Notably, we observed a significant increase in the use of XAI techniques for DRM in 2022 and 2023, emphasizing the growing need for transparency and interpretability. Through a rigorous methodology, we offer key research directions that can serve as a guide for future studies. Our recommendations highlight the importance of multi-hazard risk analysis, the integration of XAI in early warning systems and digital twins, and the incorporation of causal inference methods to enhance DRM strategy planning and effectiveness. This study serves as a beacon for researchers and practitioners alike, illuminating the intricate interplay between XAI and DRM, and revealing the profound potential of AI solutions in revolutionizing disaster risk management

Quantile-based Maximum Likelihood Training for Outlier Detection

Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like autonomous driving and video surveillance systems. Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning. Furthermore, unsupervised generative modeling of inliers in pixel space has shown limited success for outlier detection. In this work, we introduce a quantile-based maximum likelihood objective for learning the inlier distribution to improve the outlier separation during inference. Our approach fits a normalizing flow to pre-trained discriminative features and detects the outliers according to the evaluated log-likelihood. The experimental evaluation demonstrates the effectiveness of our method as it surpasses the performance of the state-of-the-art unsupervised methods for outlier detection. The results are also competitive compared with a recent self-supervised approach for outlier detection. Our work allows to reduce dependency on well-sampled negative training data, which is especially important for domains like medical diagnostics or remote sensing.Comment: Code available at https://github.com/taghikhah/QuantO

Understanding digital capabilities and their impacts on Australian agri-food supply chain resilience: Engineering vs. socio-ecological thinking

Many agri-food supply chains (SCs) are vulnerable to sudden disruptions (i.e., shocks), which can have devastating impacts. SC member firms’ digital capabilities (as routinized and integrated resource bundles) have the potential to address these challenges, yet their nature and differentiated impacts on engineering versus socio-ecological resilience are not well understood in the present literature. The purpose of this study is to address these gaps. First, a comprehensive taxonomy of five digital capabilities (labelled LogisticsTech, SecureData, ClientValue, InsightDecision, and InnovateTech) is developed, along with a set of nine resilience criteria/sub-criteria useful for resiliency evaluation. This was achieved through using a narrative literature review, a content analysis, validated by a Delphi study, and semi-structured interviews. Second, the relative impacts of these capabilities on resilience are assessed through pairwise comparison, network analysis, and system dynamics modelling across six Australian agri-food SCs (Grains, Red Meat, Dairy, Horticulture, Seafood, and Wine). The study findings reveal that Australian agri-food SC members’ digital capabilities play distinct but interconnected roles in enhancing resilience, with their impact varying across persistence (engineering), adaptation, and transformation (socio-ecological) over different time frames. LogisticsTech is crucial for short-term, while SecureData safeguards long-term persistence. Moreover, InsightDecision supports immediate adaptation, ClientValue facilitates long-term adaptation, and InnovateTech drives systemic transformation and future-ready SCs. The study offers strategic insights for managers and policymakers to align digital technology adoption with resilience objectives

Predictive digital twin technologies for achieving net zero carbon emissions: a critical review and future research agenda

Purpose: Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases. Design/methodology/approach: This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio. Findings: Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction. Practical implications: The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing. Originality/value: This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference

Where does theory have it right? A comparison of theory-driven and empirical agent based models

Computational social science has witnessed a shift from pure theoretical to empirical agent-based models (ABMs) grounded in data-driven correlations between behavioral factors defining agents’ decisions. There is a strong urge to go beyond theoretical ABMs with behavioral theories setting stylized rules that guide agents’ actions, especially when it concerns policy-related simulations. However, it remains unclear to what extent theory-driven ABMs mislead, if at all, a choice of a policy when compared to the outcomes of models with empirical micro-foundations. This is especially relevant for pro-environmental policies that increasingly rely on quantifying cumulative effects of individual behavioral changes, where ABMs are so helpful. We propose a comparison framework to address this methodological dilemma, which quantitatively explores the gap in predictions between theory-and data-driven ABMs. Inspired by the existing theory-driven model, ORVin-T, which studies the individual choice between organic and conventional products, we design a survey to collect data on individual preferences and purchasing decisions. We then use this extensive empirical microdata to build an empirical twin, ORVin-E, replacing the theoretical assumptions and secondary aggregated data used to parametrize agents’ decision strategies with our empirical survey data. We compare the models in terms of key outputs, perform sensitivity analysis, and explore three policy scenarios. We observe that the theory-driven model predicts the shifts to organic consumption as accurately as the ABM with empirical micro-foundations at both aggregated and individual scales. There are slight differences (±5%) between the estimations of the two models with regard to different behavioral change scenarios: increasing conventional tax, launching organic social-informational campaigns, and their combination. Our findings highlight the goodness of fit and usefulness of theoretical modeling efforts, at least in the case of incremental behavioral change. It sheds light on the conditions when theory-driven and data-driven models are aligned and on the value of empirical data for studying systemic changes