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The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country.

A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift.

Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind.

This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.

The paper aims to investigate key aspects of climate change adaptation strategies of Multinational Corporations (MNCs) across two different climate-vulnerable country contexts, developed, i.e. Australia and developing, i.e. Bangladesh, while identifying the key factors affecting the formulation and implementation of such strategies.

The research uses a qualitative research method using interviews and document analysis while considering distinctive factors manifest in Australia and Bangladesh and focussing on the agricultural seed business sector.

The research reveals that no specific pattern of adaptation strategies exists across MNCs. They either follow a proactive “deliberate” strategy or a reactive “emergent” strategy. MNCs also follow a distinct strategy, “subliminal”, i.e. unintended or inadvertent strategy, by following the “business as usual” approach.

In recent years, many MNCs have started embracing strategies to reduce their negative environmental footprint but barely adopted any formal strategies to adapt to climate change impacts on their business operations. This study provides insights into the existing climate change adaptation strategies of MNCs, which could be beneficial for companies in better planning and implementing their existing as well as future climate change adaptation strategies.

Based on a developed-developing country comparison and together with a novel focus on the agricultural seed business sector, the paper has used a variety of business strategies in providing insights and understanding of the status of MNC climate change adaptation strategies. The research has identified and coined the term, “subliminal” or unintended strategy as a new addition to the MNC adaptation strategy literature.

The purpose of this research is to test the effectiveness of integrating Grasshopper 3D and measuring attractiveness by a categorical based evaluation technique (M-MACBETH) for building energy simulation analysis within a virtual environment. Set of energy retrofitting solutions is evaluated against performance-based criteria (energy consumption, weight and carbon footprint), and considering the preservation of the cultural value of the building, its architectural and spatial configuration.

This research addresses the building energy performance analysis before and after the design of retrofitting solutions in extreme climate environments (2030–2100). The proposed model integrates data obtained from an advanced parametric tool (Grasshopper) and a multi-criteria decision analysis (M-MACBETH) to score different energy retrofitting solutions against energy consumption, weight, carbon footprint and impact on architectural configuration. The proposed model is tested for predicting the performance of a traditional timber-framed dwelling in a historic parish in Lisbon. The performance of distinct solutions is compared in digitally simulated climate conditions (design scenarios) considering different criteria weights.

This study shows the importance of conducting building energy simulation linking physical and digital environments and then, identifying a set of evaluation criteria in the analysed context. Architects, environmental engineers and urban planners should use computational environment in the development design phase to identify design solutions and compare their expected impact on the building configuration and performance-based behaviour.

The unavailability of local weather data (EnergyPlus Weather File (EPW) file), the high time-resource effort, and the number/type of the energy retrofit measures tested in this research limit the scope of this study. In energy simulation procedures, the baseline generally covers a period of thirty, ten or five years. In this research, due to the fact that weather data is unavailable in the format required in the simulation process (.EPW file), the input data in the baseline is the average climatic data from EnergyPlus (2022). Additionally, this workflow is time-consuming due to the low interoperability of the software. Grasshopper requires a high-skilled analyst to obtain accurate results. To calculate the values for the energy consumption, i.e. the values of energy per day of simulation, all the values given per hour are manually summed. The values of weight are obtained by calculating the amount of material required (whose dimensions are provided by Grasshopper), while the amount of carbon footprint is calculated per kg of material. Then this set of data is introduced into M-MACBETH. Another relevant limitation is related to the techniques proposed for retrofitting this case study, all based on wood-fibre boards.

The proposed method for energy simulation and climate change adaptation can be applied to other historic buildings considering different evaluation criteria and context-based priorities.

Context-based adaptation measures of the built environment are necessary for the coming years due to the projected extreme temperature changes following the 2015 Paris Agreement and the 2030 Agenda. Built environments include historical sites that represent irreplaceable cultural legacies and factors of the community's identity to be preserved over time.

This study shows the importance of conducting building energy simulation using physical and digital environments. Computational environment should be used during the development design phase by architects, engineers and urban planners to rank design solutions against a set of performance criteria and compare the expected impact on the building configuration and performance-based behaviour. This study integrates Grasshopper 3D and M-MACBETH.

Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

As Australians experience more fierce and frequent natural disasters, there are urgent calls for businesses to meaningfully respond to climate change. Australian financial and professional services employees occupy an ambiguous space as climate mitigation measures have different economic implications for their clients. The purpose of this paper is to investigate how Australian professionals experience climate change and respond to the issue within their workplace.

This mixed methods study applies a systems thinking framework to investigate: how do professionals’ experiences of the issue of climate change and the workplace influence their cognitions, emotions and behaviour? And in particular, what psychosocial antecedents precede voicing climate concern?

Firstly, a survey of professionals (N = 206) found social norms, perceived behavioural control and biospheric values, but not attitudes, significantly predicted prohibitive green voice. Middle managers were significantly likely to voice climate concern, whereas senior managers were significantly likely to express climate scepticism. Ten professionals were then interviewed to gain a contextualised understanding of these trends. Interpretive phenomenological analysis identified five interrelated themes: (1) active identity management, (2) understanding climate change is escalating, (3) workplace shapes climate change response, (4) frustration and alienation and (5) belief that corporations prioritise profit.

Findings are discussed in relation to how employees may both embody and adapt their organisations. These results have implications for understandings of workplace meaningfulness and organisational risk governance.

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.

This study aims to discuss climate change, by modifying the timing of several agricultural operations, reduce the efficiency and yield of inputs leading to a lower production level. The reduction of the effects of climate change on production yields and on farmers' technical efficiency (TE) requires the adoption of adaptation strategies. This paper analyses the impact of climate change adaptation strategies adopted on maize farmers' TE in Benin.

This paper uses an endogeneity-corrected stochastic production frontier approach based on data randomly collected from 354 farmers located in three different agro-ecological zones of Benin.

Estimation results revealed that the adoption of adaptation strategies improve maize farmers' TE by 1.28%. Therefore, polices to improve farmers' access to climate change adaptation strategies are necessarily for the improvement of farmers' TE and yield.

The results of this study contribute to the policy debate on the enhancement of food security by increasing farmers' TE through easy access to climate change adaptation strategies. The improvement of farmers' TE will in turn improve the livelihoods of the communities and therefore contribute to the achievement of Sustainable Development Goals 1, 2 and 13.

This study contributes to theoretical and empirical debate on the relationship between adaptation to climate change and farmers' TE. It also adapts a new methodology (endogeneity-corrected stochastic production frontier approach) to correct the endogeneity problem due to the farmers' adaptation decision.

The purpose is to stimulate scholarship in the strategic management field that accounts for conditions implied by projected impacts of climate change.

Following conceptual logic, the article analyses how changes in the strategic environment brought about by climate change may challenge current strategic management theory. It develops avenues for theory development based on expanding the field’s scope and extending its limits of applicability.

The article highlights the extent to which the strategy field has evolved in a stable empirical context, despite its attention to dynamism and hence is less well aligned with potentially pervasive new pressures and impacts. It sets out a rationale for moving beyond symbolic environmentalism, possibilities to harness cognitive and behavioural insights, dilemmas in strategic innovation and the empirical potential of non-mainstream contexts.

Firms and organisations can expect widespread systemic effects from climate change that challenge established ways of operating. The article explores how strategic management could better support strategists in navigating these shifts such that firms can continue to thrive.

The article approaches the issue of climate change specifically from the perspective of strategic management of firms rather than as policy or social advocacy. It focuses on pressures and characteristics that distinguish climate change from other environmental and social impacts on firms.

The effects of population growth in the developing world and climate change have increased the stress on available water resources. The majority of Rajshahi city, Bangladesh, is facilitated with groundwater withdrawal. As Bangladesh is a country of monsoon climate, reserved rainwater can be contributed as an alternative to extracted groundwater. This study aims to develop a framework for rooftop rainwater harvesting (RRWH) for domestic purposes and estimate the appropriate size of the storage tanks and their costs required to fulfill the annual drinking and cooking water demands through RRWH in Rajshahi city of Bangladesh.

A total of 100 single-story residential dwellings with varying rooftop areas were surveyed for the projection of RRWH potential. The relationship between the size and cost of a water tank and the rooftop areas of different houses is expressed using a general mathematical equation. Cost estimates for the proposed RRWH system for all houses have been completed, and a cost model illustrating the relationship between rooftop or catchment area and associated cost of RRWH system has been developed.

This study reveals that a maximum of 110.75 m³/year rainwater can be collected from a 100 m² rooftop area of Rajshahi city. Moreover, this study finds that such harvesting of rainwater can reduce municipal water supply to the extent of almost 75%. Water samples collected from rooftops also revealed that if germs were removed through bacteria treatment, the collected rainwater potentially can be used for drinking and cooking purposes.

The novelty of this study is that it focused mainly on how significant RRWH can be to meet people’s daily required amount of water for household purpose and ascertain the cost reduction using the RWH method. This paper also is unique as it assessed the volume of the storage tank that is sufficient to distribute the necessary amount of water for drinking and cooking purpose as a sustainable alternative source in the dry season.

This study assesses the extent to which integrated extension services contribute to the adoption of climate-smart agriculture (CSA) innovations within the cotton value chain in Burkina Faso.

To address the research question, a probit multivariate econometric model with sample selection is utilized. The model is applied to a random sample of farmers (n = 510), and the endogeneity is addressed through a control function approach.

The study highlights the central role of value chains, particularly in the cotton sector, in overcoming resource scarcity through integrated extension services. Findings show that smallholder farmers who benefit from sound extension services are more willing to adopt and diversify CSA technologies. These include improved seeds, conservation techniques, adapted planting dates and mechanization. This study confirms the synergistic nature of these technologies and emphasizes that effective climate risk mitigation depends on the combined adoption of CSA technologies.

The use of cross-sectional data limits the analysis of long-term farmer behavior, and due to data limitations, the focus was primarily on the contributions of cotton companies and farmers to climate risk mitigation. Future research using panel data across the value chain could provide a more robust insights for policy decision-making.

The study contributes to the existing body of knowledge by emphasizing the crucial role of integrated extension services within the cotton value chain in developing countries. This highlights the critical benefits for farmers and emphasizes the need to diversify modern technologies to effectively combat climate change and its variability in agriculture.