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Energy efficiency can help facing the climate crises. Yet the energy intensity has decreased more slowly than required to achieve climate goals. Based on this premise, we build an agent-based model to study the effects of different energy efficiency policies. Policies analysed range from indirect policies – taxes, incentives, and subsidies – to direct technological policies, where a public research laboratory invests in R&D to establish a new technological energy efficiency paradigm. Results reveal that, although most of the policies effectively reduce energy intensity, the research lab is the most efficient in promoting energy efficiency without negative impacts on macroeconomic and public finance conditions. However, the success of this policy requires a long-term commitment, highlighting the importance of complementing this strategy with more "ready to use" measures. The findings also indicate that most policies do not induce significant macroeconomic rebound effects. Concerns about macroeconomic rebound effects may likely be overstated. • Improvements in energy efficiency help face the ongoing climate and energy crises. • Yet, energy intensity has declined more slowly than needed to meet climate goals. • The paper builds an agent-based model to study different energy efficiency policies. • Indirect policies work but are outperformed by direct technological policies. • The macroeconomic rebound effect induced by most of the policies is rather small. [ABSTRACT FROM AUTHOR]

One of the technological responses to concern about the healthiness and sustainability of red meat consumption as well as growing global food insecurity has been the development of plant-based meats. While still niche industries, plant-based meats are increasing in diversity and visibility and are often accompanied by scientific claims about their potential contribution to sustainability, health and nutrition. In this paper we analyse these claims from the perspective of critical metrology, looking not only at the state of knowledge about plant- and animal-based beef but also how knowledge is being produced. Such research is vital in understanding not only what is known about a topic but also the limitations of that knowledge in terms of influences and gaps, to shape how research can be done better. The research is based on a scoping review of scientific papers that compare the sustainability and nutritional properties of plant- and animal-based beef, with a particular focus on the Australian context. Our analysis shows that there are some points of consensus, such as plant-based beef having lower greenhouse gas emissions than animal-based beef and that plant-based burgers have lower total fat and saturated fat than animal-based burgers. However, we also find points of difference due to variability in production systems, products and approaches to measurement. We discuss how a metrological regime is forming that bears the imprint and interests of the industries involved, influencing the metrics and geographies that are studied and creating critical absences in understanding. [ABSTRACT FROM AUTHOR]

Roads play a vital role in transportation systems, and their construction and maintenance expenses are high. Those reasons make crucial the examination of factors that accelerate road deterioration and those factors' negative impacts on societies. Climatic-related elements, especially temperature and precipitation, significantly affect the quality and lifespans of roads. The Intergovernmental Panel on Climate Change has reported that increasing greenhouse gas (GHG) emissions is causing changes in climate and that the rate of change has accelerated during the past several decades. To assess the impacts of changing climatic parameters on flexible-pavement performance, this study used 32 climate models to evaluate the performance of interstate, primary, and secondary roads across 11 diverse locations with varying traffic and climatic conditions. The study's findings reveal that changing climate exacerbates pavement distresses, leading to reduced pavement lifespans and increased numbers of reconstruction projects, which in turn raise demands for materials and equipment and contribute to higher GHG emissions. The effects of changing climate and changing climate's associated economic and environmental costs make the application of various engineering solutions to enhance pavement resilience essential, as described in this paper, in the absence of comprehensive emissions reduction strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

• The complex interconnectedness of climate change, energy demand, and health and long-term care poses current and future cascading challenges for the resilience of older people. • Key interconnections include a concern for the impact of frequent and intense weather events parallel to a global increase in life expectancy and social justice issues connected with strained resources and public infrastructure. • Comprehensive, targeted policies are needed on the national and global level that incentivize sustainable practices, enhance climate resilience, and support vulnerable populations in the Global South, and that mitigate the social justice implications of climate change for older adults. Challenges faced by many countries are energy insecurity, climate change, and the health and long-term care of growing numbers of older people. These challenges are increasingly intersecting with rising energy prices, aging populations, and an increased frequency and intensity of extreme climate events. This paper gives a deeper understanding of the current and predicted interconnections among these challenges through narrative-driven content and thematic analysis from workshops with a diverse group of international stakeholders from the Global North and Global South. Narratives emerged highlighting a complex nexus of interconnections and presenting critical action areas. Targeted local and global policies and interventions are needed to alleviate stress on health systems, encourage the integrated uptake of clean energy sources, and uphold social justice across all economies. Professionals can use this work to inform the design and implementation of effective interventions and increase the resilience of older adults by better preparing for systemic risks. [ABSTRACT FROM AUTHOR]

This research paper proposes a novel multi-model approach, integrating the CARIMA-SARIMA-GPM framework, to assess the combined impacts of climate change and land use change on the potential of concentrated photovoltaic (CPV) systems. By considering both climatic variables and land use patterns, this study aims to provide a comprehensive understanding of how these factors influence CPV performance in the context of a changing environment. The proposed methodology offers valuable insights into the future viability and sustainability of CPV technology, enabling informed decision-making for policymakers, energy planners, and investors in the Middle East and Africa. As a result, the ability of the hybrid evolutionary CARIMA-SARIMA-GPM to predict the potential of CPV energy output for assessing the impacts of climate change on it was investigated in Alice Springs, the Middle East, and Africa. The outcome showed that the hybrid model significantly outperformed the other machine learning approaches. The fitted model was used to assess the potential impacts of climate change on CPV generation in Alice Springs, Australia, as well as the Middle East's and Africa's comparable climatic conditions. According to the study, climate change had the greatest impact on solar CPV energy production in Alice Springs, where it decreased the most by 8.577% under moderate forcing scenarios (SSP245) during the boreal summer season; moderately in the Middle East, where it decreased the mode by 2.316% under mitigation scenarios (SSP126) during the boreal summer season; and extremely minimally in Africa, where it decreased the mode by 1.263% under the far future sequencing period (2051–2099). Climate change also increased solar CPV energy production significantly in the Middle East in the far future sequencing period (2051–2099), as well as in Alice Springs, Australia, and Africa in the near future sequencing period (2015–2050). The strongest forcing scenario (SSP585) increased by 7.644% during the boreal autumn season in Africa; moderately increased by 6.502% during the boreal spring season in the Middle East; and had the least beneficial effects in Alice Springs, Australia, with increases of 5.538% during the boreal winter season. On an annual basis, all three regions showed a similar trend. Climate change (CLC) and urban expansion (URE) were also investigated in the Middle East and Africa for their effects on changes in solar CPV energy output. URE had a greater impact in Africa than the Middle East under the effective scenario, with a URE value of 45.45% for Africa and 20.15% for the Middle East, whereas CLC had a greater impact in the Middle East than Africa, with a CLC value of 29.01% compared to 5.47% for Africa. CLC and CPV residual factors, on the other hand, have a greater impact in the Middle East than in Africa, with effects of 29.01% and 50.83%, respectively, compared to 5.47% and 49.09%. The potential difference that drives the remediation of specific pollutants lies in the application of advanced technologies and sustainable practices. By exploring innovative solutions, such as using renewable energy sources like concentrated photovoltaic (CPV) systems, we can effectively mitigate the impacts of climate change and land use changes on pollutant concentrations. These technologies have the potential to significantly reduce pollution levels and create a cleaner and healthier environment for future generations. Assessing the CPV potential in different regions like Alice Springs, Australia, the Middle East, and Africa allows us to identify areas with high solar energy resources that can be harnessed for efficient pollutant remediation. Implementing prompt climate mitigation and adaptation measures is crucial for achieving a net-zero energy transition in the Middle East and Africa by 2050. In this context, prioritizing solar energy as the primary source of renewable energy is imperative for successful low-carbon economic planning in these regions. • CARIMA-SARIMA-GPM outperforms other applicable models • By 2100, the CPV potential in Alice Springs could increase by 0.082–5.338% at 1.5 °C • By 2100, the Middle East's CPV potential could increase by 0.021–0.221% at 1.5 °C. • By 2100, Africa's CPV potential could increase by 0.133–7.644% at 1.5 °C. • Climate change has harmed Alice Springs and the Middle East more than Africa. [ABSTRACT FROM AUTHOR]

• Integrating an agent-based model into a large-scale distributed hydrologic model. • Evaluating the co-evolution of the CNHS at multiple spatial scales. • Demonstrating the trade-off among FEW sectors under climate change. Managing water resources to meet increasing energy and food demands while maintaining environmental sustainability under climate change is a major challenge, especially when this nexus occurred in a coupled natural–human system (CNHS), where heterogeneous human activities affect the natural hydrologic cycle and vice versa. The relevant research has been limited by the lack of models that can effectively integrate human dynamics and hydrologic conditions with spatial details to examine co-evolutionary systems. To address this challenge, this paper develops a modeling framework that integrates an agent-based model (ABM; human behavior model) into a large-scale, process-based distributed hydrologic model to simulate human decisions endogenously in the hydrologic cycle. We then apply the Decision Scaling approach, an ex-post scenario analysis method, with our integrated model to study the bidirectional feedback of the CNHS under future changing climate conditions. With the Columbia River Basin (CRB) selected as the case study area, the calibration results show that the integrated model can simultaneously capture the historical irrigated water consumption and streamflow dynamics. Modeling results show that the trade-off between irrigated water consumption, hydropower generation, and streamflow will become more pronounced under hotter and wetter climate conditions at both the entire basin and regional (states and provinces) levels. Special attention should be given to "temperature thresholds" of different regions when the trade-off pattern started. The trade-off results can potentially inform the Columbia River Treaty renegotiation and provide insights for long-term water management policies. [ABSTRACT FROM AUTHOR]

This paper applies computational linguistics learning methods to the banking industry and climate change fields. We introduce our data-driven framework, climateBUG, with the aim of detecting latent information about how banks discuss their activities related to climate change using natural language processing (NLP). This framework consists of an ingestion pipeline, a configurable database, and a set of API's. In addition, climateBUG offers two standalone components, namely a unique annotated corpus of approximately 1.1M statements from EU banks' annual and sustainability reporting and a deep learning model adapted to the semantics of the corpus. When benchmarking on classification performance, our model outperforms other models with similar scopes due to its stronger domain relevance. We also provide examples of how the framework can be applied from a user perspective. • A data-driven framework to detect qualitative information in banks' annual reporting. • Natural language processing is applied to non-financial reporting and sustainability. • Framework developed with an interdisciplinary approach and relevant domain knowledge. • Framework includes annotated data and learning model as standalone components. [ABSTRACT FROM AUTHOR]

Given the significant energy consumption and carbon emissions associated with buildings, there is increasing interest in improving sustainability of building operations to reduce impact on climate change. A common goal is to operate buildings as "net-zero" energy users in which all energy consumed by the building is balanced against renewable energy purchased from the grid or produced on site. To achieve net-zero status, many buildings will require significant retrofit so as to both reduce energy consumption in the absolute sense and provide the remainder without consuming fossil fuels. Thus, multi-year planning is required to ensure that goals can be met on time. In addition, due to the inherent uncertainty associated with energy consumption and generation, actually achieving net-zero energy use may require discretionary curtailment actions to be taken, and deciding whether such actions are necessary can be challenging. To address these challenges, we propose in this paper a design optimization and operational planning strategy to make the decisions needed to achieve sustainability goals in buildings. The strategy can be applied to schedule design changes over a long horizon to meet annual targets, and it can also be applied in closed loop on a shorter horizon to determine whether curtailment is needed to stay on track. We discuss the formulation of the optimization problem, solution methods, and modeling approaches for key parameters. Application of the strategy is illustrated via examples. Overall, this approach will help automate planning that is often done manually, allowing buildings to take a significant leap forward toward achieving their sustainability goals. • Achieving sustainability goals in buildings requires multi-year planning. • The required design and operational decisions can be made via optimization. • Key problem parameters can be obtained from data-driven modeling. • The strategy allows sustainability goals to be met at minimum cost. [ABSTRACT FROM AUTHOR]

This study explores the impact of political uncertainty on sustainable urban development by examining carbon emission trading systems (ETS) in four major markets in China (Beijing, Shanghai, Guangdong, and Hubei) from 2014 to 2022. As an alternative to carbon taxes, carbon ETS markets have become increasingly popular due to their success in reducing greenhouse gas emissions. However, their effectiveness is often hindered by political instability and uncertainty. Utilising logistic regression and AR(1)-GARCH estimations, we identify a negative relationship between political uncertainty and carbon trading volume. Our study also reveals significant variations in the responses of these markets to political uncertainty. The paper contributes to the understanding of how ETS markets operate in a complex and constantly changing political environment. We suggest that policymakers need to consider the impact of political uncertainty on carbon trading when designing and implementing urban policies that promote sustainable development. Additionally, our research contributes to the development of urban policies that can be effectively implemented in both developed and developing regions. • Local government official turnovers are used as the measurement of political uncertainty. • Daily transaction data from four carbon ETS markets (Beijing, Shanghai, Hubei and Guangdong) in China • Considered the probability and proportion of zero transaction day • Logistic regression and AR(1)-GARCH estimations • A negative relationship between political uncertainty and carbon transaction volume [ABSTRACT FROM AUTHOR]

Forests are indispensable in mitigating climate change due to their ability to sequester and store large amounts of carbon. Forest restoration and maintenance activities are crucial for retaining and enhancing this capacity. To promote sustainable practices, stakeholders need to assess the potential impacts of forestry operations. But in-depth comprehension of the forest dynamics is challenging due to the complex interactions between various ecosystem components. High-fidelity, immersive visualizations can support sense- and decision-making processes by facilitating the exploration of scientific and non-scientific data in the respective spatial, temporal, and semantic contexts. However, producing accurate representations of diverse ecosystems is complex and time-consuming. In this paper, we introduce a forest visualization prototype providing realistic representations of forest stands and different simulated scenarios. The utilized data-driven and procedural approach ensures efficiency, reproducibility, and accuracy. Furthermore, we present an array of requirements for realistic forest visualizations gathered through questionnaires and interviews with forestry experts. • Realistic forest visualization created by integrating real-world data in game engine. • Data-driven methods deployed for efficiency, reproducibility, and accuracy. • Procedural ground surface modeling using geographical datasets. • Balance between scalability and realism achieved through game engine mechanics. • Common expert needs: forest structure visualization, simulation, educational content. [ABSTRACT FROM AUTHOR]

Climate-smart agriculture (CSA) aims to address climate change, climate variability, and food security while sustaining productivity. The literature on acceptance and adoption of CSA technologies recognizes the importance of the policy environment in shaping farmers' decisions, particularly the policy mix, including economic, regulatory, and information instruments to stimulate CSA acceptance. Behavioral models have been used to better explain CSA acceptance, however, only a few studies integrate farmers' behavioral drivers and their policy mix appraisal, which in combination, are important determinants for the successful acceptance of CSA. This paper proposes a model that integrates behavioral drivers and policy mix appraisal influencing the acceptance of CSA technologies. We aim to examine how farmers' behavioral drivers and their appraisal of the policy environment influence the acceptance of CSA technologies and practices. We studied the Costa Rican coffee sector and conducted 523 surveys with coffee farmers and two focus groups with experts, extension agents, and cooperatives. An ordered probit model was used to identify factors explaining CSA acceptance. The results indicate that besides the influence of behavioral drivers, policy consistency and comprehensiveness and the type of instrument targeting farmers' behaviors play an important role in explaining CSA acceptance. Our results suggest that a positive appraisal of policy consistency and comprehensiveness are important for increasing farmers' acceptance of CSA and sustainable practices. This deepens earlier thinking on "policy packages" effectiveness by showing that the farmers' appraisal of the overall policy mix influences their decisions to engage with CSA. Our study shows the importance of considering system context effects (policy environment) on farmers' decision-making. Since the integration of behavioral drivers and the appraisal of the policy mix characteristics is relatively underexplored for CSA, our empirical results may help to unravel farmers' decision-making processes. Thus, it can be used for rethinking and adjusting policy interventions toward more balanced and comprehensive policy mixes, as it enables feedback from policy implementation and can further induce policy learning. [Display omitted] • We determine the factors influencing farmers' acceptance of Climate-smart technologies • CSA acceptance results from behavioral predictors, socioeconomic drivers, and policy appraisal • Policy consistency and comprehensiveness are key drivers for increasing farmers' acceptance of CSA • Integrating behavioral drivers and policy appraisal enhances the understanding of system contextual effects on farmers' decisions [ABSTRACT FROM AUTHOR]