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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.

In Argentina, soy and maize represent 28% of the total country exports, affecting the balance of payments, international reserves accumulation and sovereign credit risk. In the past 10 years, three extreme and moderate droughts have affected the agricultural areas, causing significant losses in soybean and maize production. This study aims to estimate the economic impact generated by different drought levels for soy and maize production areas through a financial perspective that allows the estimation of the cash flow and income losses.

By analyzing the extreme deviations in yields during dry periods, the losses generated by droughts were valuated among 183 departments nationwide.

The aggregated results indicated a total loss of US$24.170m, representing 57.45% of the international reserves of the Argentinean Central Bank in 2021. This estimate shows the magnitude of the climate impact on the Argentinean economy, indicating that severe droughts have macroeconomic impacts, with the external sector as the main transmission channel in an economy with historic restrictions on the balance of payments, international reserve accumulation and sovereign credit risk.

This study analyses the macroeconomic impact of drought on Argentinean soybean and maize production.

Climate change and its imminent threat to human survival adversely impact the agriculture sector. In an impoverished country like The Gambia, economic costs of climate change are colossal. This study aims to establish a computable general equilibrium (CGE) model for The Gambia’s agriculture sector to examine the effects of climate change on crops, livestock and sea-level rise.

This study used a CGE model with other climate change impact models to compute the impacts of climate change on The Gambia’s agriculture sector. The social accounting matrix calibrates the results from the various models, thereby generating the baseline results which exemplify a “steady-state” and policy shock results illustrating the medium- and long-term effects of climate change on the country’s agriculture sector.

The baseline results indicate the status quo showing the neglect of the agriculture sector due to limited investment in the sector. Hence, the sector is the “hardest hit” sector as a result of climate change. When the model factored in climate change in the medium term (2055) and long term (2085), the macroeconomic indicators of gross domestic product, national savings, wages, disposable income and consumer price index deteriorated, elucidating the vulnerability of the economy to climate change. The consumption of groundnuts, cattle and fish will decline by 5%, 5% and 4%, respectively, in the long term. However, the production of all agricultural commodities will decline by an average of 35% for the same period. The results for international trade show that exportation would decline while importation will increase over time. The general price level for agricultural commodities would increase by 3% in 2055 and 5% in 2085. Generally, the results manifest the severity of climate change in the agriculture sector which will have a multiplier effect on the economy. The impact of climate change would result in agriculture and economic decline causing hunger, poverty and human misery.

The caveat of this study revealed the nuances not captured by previous Gambian climate change studies, thus the novelty of the study.

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 study results and recommendations will have a paramount significance for policymakers, policy advocates, development planners and practitioners who may be in need of such information for reconsideration, evaluation and inclusion into their respective development and humanitarian programming and operational strategies. Above all, the study result has further provided the local community with viable adaptation strategies to climate-induced changes in the study area.

This study was conducted to measure the livelihood vulnerability of Borana pastoralists to climate change and variability in southern Ethiopia. Pastoralists’ households were sampled using multistage sampling techniques. A total of 27 socio-economic and biophysical indicators were used to reflect vulnerability components: adaptive capacity, exposure and sensitivity. Principal component analysis was used to develop weights for indicators and to produce livelihood vulnerability index to classify households according to their level of vulnerability. Ordinal logistic regression was used to identify the determinants of vulnerability to climate-induced stresses.

The results showed that 24.4% of households were highly vulnerable, 60.3% were moderately vulnerable and 15.3% of households were less vulnerable to climate-induced stresses. Factor estimates of the logistic model further revealed that early warning information, bush encroachment, coping strategy, temperature, drought frequency, provision of humanitarian services and food shortage during the normal season of the year have a significant influence on vulnerability in the study area.

The study’s results and recommendations will be of great significance to policymakers, development planners, and practitioners who require such information for reconsideration, evaluation, and inclusion in their respective development and humanitarian program and operational strategies. Most importantly, the study’s findings have provided the local community with practical adaptation strategies to climate-induced changes in the study area.

The study explored pastoralist perception of climate change and variability and measured the livelihood vulnerability of pastoralists’ households to climate change and variability and finally investigated viable adaptation and coping strategies in the study area.

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.

This study aims to provide a review for scour in complex rivers and streams with coarser bed material, steep longitudinal bed slopes and dynamic environments, in the interest of the safety and the economy of hydraulic structures. The knowledge of scour in such geographical complexities is very crucial for a comprehensive understanding of scour failures and for establishing definitive criteria to bridge this major research gap.

The existing available literature shows significant work done in case of silt, sand and small sized coarser bed material but any substantial work for bed material of gravel size or above is lacking, resulting in a wide gap. Though some researchers have attempted to explore possibilities of refining the existing models by adding pier size, shape, sediment non-uniformity and armouring effects, which otherwise have been given a miss by the various researchers, including the pioneer in the field Lacey–Inglis (1930). But still, a rational model for scour estimation in such complex conditions for global use is yet to come. This is because all the parameters governing the scour have not been studied properly till date as is evident from the globally available literature and is witnessed in the field too, in recurrent failure of hydraulic structures especially bridges.

The researchers presume that the finer materials move only as a result of erosion. However, in actual field conditions, it has been observed that the large-sized stones also roll down and cause huge erosion along the river bed and damage the hydraulic structures, especially in the steep river/stream beds along hilly slopes. This fact has been overlooked in the models available globally and has been highlighted only in the current work in an attempt to recognize this major research gap. A study carried out on a number of streams globally and in Jammu and Kashmir, India also, has shown that in steep river and stream beds with bed material consisting of gravel size or greater than gravel, large scour holes ranging from 1 m to 5 m were created by furious floods, and due to other unknown forces along the channel path and near foundations of hydraulic structures.

To the best of the authors’ knowledge, this work is purely original.

The first aim of this study is to show the impact of global warming on temperature and precipitation changes in Algeria. The second objective is to exhibit the strategy planned by the country to mitigate these effects on water resources in the future.

This research assesses the expected changes in temperature, precipitation and SPEI index, over Algeria (16 weather stations), between two horizons (2030, 2050), by using an ensemble of 16 general circulation models under RCP2.6 and RCP8.5 scenarios.

More warming and drought will be experienced under RCP8.5 than RCP2.6 scenario. The highest warming is observed at the Southern stations. However, the lowest precipitation is projected in the western stations. The results of SPEI calculation indicate that the severity of drought spread progressively across time and space. The highest values were observed over 2050 with values varied between 0.15 and −2.08 under RCP2.6 scenario, and range from −0.73 to −2.63 under RCP8.5. These results indicate that Algeria is highly vulnerable to the impact of climate change on water resources, which stressed the need to develop a strategy against this situation.

This study is one of the first to simulate the future climate changes over 16 Algerian weather stations by using an average of 16 general circulation models data, under two RCP scenarios. This study shows the 2030 water development strategy to mitigate the effect of drought and water scarcity on different sectors.

Environment, social, governance (ESG) has taken on increased importance in real estate investment in recent years, with benchmarking ESG being critically important for more informed real estate investment decision-making. Using 60 stakeholder interviews with senior real estate executives, this paper examines the strategic issues regarding benchmarking ESG in real estate investment; specifically, identifying areas going forward where ESG benchmarks need to be improved. This includes the issues of granularity, climate resilience and climate risk, as well as an increased focus on outcomes and performance, and using best practice procedures in delivering ESG in real estate investment.

In total, 60 stakeholder interviews were conducted with key real estate players globally to assess the use of ESG benchmarking in real estate investment at various levels (asset/fund-level, listed real estate, delivery, reporting and internal benchmarking), across regions and across different types of real estate investment players (real estate fund manager, real estate investment trust (REIT), institutional investor and real estate advisor). This enabled key strategic insights to be identified for improved ESG benchmarking practices in real estate investment going forward.

There was clear evidence of the need for improved benchmarks for ESG in real estate investment. More focus was needed on performance, outcomes and impacts, with a stronger focus on granularity around the issues of climate resilience and climate risk. Improvements in Global Real Estate Sustainability Benchmark (GRESB), as well as increased attention to Task Force for Climate-Related Financial Disclosures (TCFD) were seen as important initiatives. Clear differences were also seen in the use of these ESG benchmarks on a regional basis; with Australia and Europe seen as the world leaders. These strategic stakeholder insights regarding ESG saw the development of best practice guidelines for the more effective delivery of ESG benchmarks for more informed real estate investment decision-making, as well as a series of recommendations for improving ESG benchmarking in real estate investment.

ESG benchmarking is a critical area of real estate investment decision-making today. By utilising stakeholder interviews, the strategic insights from key players in the real estate investment space are identified. In particular, this paper identifies how the current ESG benchmarks used in real estate investment need to be improved for a more critical assessment of climate resilience and climate risk issues at a more granular level. This enables the identification and delivery of more effective ESG best practice procedures and recommendations for improving ESG benchmarking in real estate investment going forward. These issues have clear impacts on ongoing capital raisings by investors, where benchmarking ESG is an increasingly important factor for real estate investors, tenants and real estate asset managers.

Based on the stakeholder interview responses, this paper has identified key areas for improvement in the current benchmarks for ESG in real estate investment. It is anticipated that an increased focus on technology and the availability of more granular data, coupled with user demand, will see more focus on assessing performance, outcomes and impacts at a real estate asset-specific level and produce a fuller range of ESG metrics, more focused on climate resilience and climate risk. This will see a more effective range of ESG benchmarks for more informed real estate investment decision-making.

Climate change has a direct impact on companies. Therefore, the scenario analysis is used to provide companies and stakeholders in this specific sector with forward-looking measures and narratives of the world's future state. This work aims to provide an independent, wide and rigorous literature review on the topics of scenario analysis and climate change, analyzing a large set of referred papers included in economic journals on the Web of Science Clarivate Analytics data source. This review, by means of a mixed approach, can help address new policy strategies and business models.

The work employs 416 abstracts and relative titles in the field of economics, employing data mining for qualitative variables and performing descriptive statistics and lexicometric measures, similarity analysis and clustering with Reinert's hierarchical method in order to extract knowledge. Furthermore, qualitative content analysis allows for the return of a comprehensive and complete universe of meaning, as well as the analysis of co-occurences.

Content analysis reveals three main classification clusters and four unknown patterns: model area, risks, emissions and energy and carbon pricing, indicating research directions and limitations through an overview with an extensive reference bibliography. In the research, the prevalent use of quantitative instruments and their limitations emerge, while qualitative instruments are residual for climate change assessment; they also highlight the centrality of transition risk over adaptation measures and the combination of different types of instruments with reference to carbon pricing.

Scenario analysis is a relatively new topic in economics and finance research, and it is under-investigated by the academy. The analysis combines quantitative and qualitative research using text analytics.