Climate-sensitive Sectors Research Articles

The Regional Climate Center Program—Past, Present, and Future: Reflections from 40 Years of Regional Climate Services

Abstract Since its inception in 1983, NOAA’s Regional Climate Center (RCC) Program has been providing timely, customized climate services for decision making across all climate-sensitive sectors. Through this 40-yr period, the RCC Program has not only seen but also has played an active role in, the evolution of climate services from the days of climate data libraries—where books of data were consulted to fulfill simple data requests—to coproduced tools that can calculate sectoral-specific, on-the-fly climate analyses in a matter of seconds. With new technologies emerging, the RCC Program is poised to build on its reputation as a trusted climate service provider by incorporating advanced methods for climate service delivery to continue to meet the needs of the nation. This publication will provide a look back at the evolution of regional climate services over the past 40 years, along with a vision for the future.

Projected changes in wet and dry extremes in the CMIP6 multi-model ensemble over the IGAD region of Eastern Africa

Wet and dry days, as well as wet and dry spells, are crucial pieces of information for rain-fed agriculture, food security, and numerous socioeconomic activities in East Africa. This study examines the projected changes in wet/dry days and wet/dry spells using data from the Multi-Model Ensemble (MME) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Statistical criteria and thresholds are used to project the changes in wet and dry days, as well as wet and dry spells, under the shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the annual, March–May (MAM), June–September (JJAS), and October-December (OND) seasons. The analysis indicates that the CMIP6 models generally overestimate the number of wet days (wet spells) across all seasons and scenarios at the 1 mm threshold. A projected 10–20% increase in total rainfall over the IGAD region is driven by an increase in rainfall intensity and the number of wet days under all scenarios during the near (2021–2050) and far (2071–2100) futures. A decrease in wet days and spells (increase in dry days and spells) is projected over South Sudan in JJAS, while an increase in wet spells is projected over Kenya, Somalia, and Sudan in OND during the near and far future compared to the baseline period. By the end of the century, wet days are projected to increase by approximately 20–30%, and dry spells are projected to decrease by 10–20% under the SSP1-2.6 and SSP5–8.5 scenarios. The EnsMean 10 CMIP6 tends to smooth out the extremes (wet or dry biases), creating a result that may appear more accurate but does not reflect the extremes or variations projected by individual models over South Sudan, Uganda, Kenya and Ethiopia. The projected patterns of wet and dry spells under SSP1-2.6 highlight the importance of mitigation efforts to limit temperature rise to below 1.5°. It is necessary to model how shifts in extreme dry and wet conditions will likely affect climate-sensitive sectors, such as agriculture and food security, in order to make well-informed decisions.

Nonlinear effects of climate risks on climate-sensitive sectors

Nonlinear effects of climate risks on climate-sensitive sectors

Climate-related risks, firm performance and role of risk reserve funds: empirical evidence in Vietnam

ABSTRACT This study examines how climate-related risks and risk reserve funds affect the financial performance of Vietnamese-listed firms in climate-sensitive sectors. Using data from 464 firms between 2003 and 2022 and employing dynamic estimation methods, the research reveals that climate change indicators, such as environmental protection taxes, extreme weather events, temperature fluctuations, and carbon emissions, negatively impact firm performance. The study also finds that increased rainfall benefits these firms. The effects of most climate indicators, except rainfall, became more pronounced after the 2015 Paris Agreement (COP 21). Additionally, firms with risk reserve funds are better equipped to mitigate climate change risks. These findings were validated through alternative econometric methods and different dependent variables. The results underscore the urgent need for corporate managers, investors, and policymakers to incorporate climate risks into decision-making processes, fostering the development of strategies to address the financial impacts of climate change.

Analysis of Rainfall Variability Using Precipitation Concentration Index for a Catchment

The PCI is a critical metric to assess the distribution and concentration of precipitation over a specified period. By analyzing historical rainfall data, this research aims to identify trends and patterns in precipitation concentration, shedding light on potential implications for water resource management and hydrological systems. The methodology involves calculating the PCI for different temporal scales and regions, allowing for a comprehensive assessment of rainfall variability. The findings contribute to a deeper understanding of precipitation dynamics, facilitating informed decision-making in climate-sensitive sectors. There are simple rainfall measures that can be used to provide information on variability and, therefore, on the climate state. These mainly include annual and monthly rainfall data and averages, which can be used to assess rainfall seasonality, variability, and the frequency of extreme events.

Bias Correction of Monthly Precipitation from different General Circulation Models Using Cumulative Density Function in Raipur District, Chhattisgarh, India

Accurate representation of precipitation patterns is crucial for understanding and adapting to these impacts. General Circulation Models (GCMs) are essential for projecting future climate scenarios but often exhibit biases in simulating precipitation, undermining the reliability of their outputs. This study focused on bias correction of monthly precipitation data from different GCMs using Cumulative Density Functions (CDFs). Bias correction techniques were employed to align model-simulated precipitation with observed data, revealing significant improvements in the accuracy of future precipitation projections. The study area, Raipur, characterized by diverse topography, served as the location for analysis. Three GCMs were selected based on their availability and participation in the CMIP6 experiment. The bias correction process involved the calculation of CDFs and equiprobability transformations, resulting in a closer match between model predictions and observations. Results showed substantial variability in monthly precipitation values across different climate models and scenarios, with distinct seasonal patterns observed. Inter-model discrepancies underscored the complexities of precipitation simulations, highlighting the need for careful interpretation of model outputs. Continued research efforts were crucial for improving the accuracy and reliability of climate model simulations for informed decision-making and planning in climate-sensitive sectors.

Assessment of Climate Change Financing Mechanisms in Cameroon: A Comprehensive Review

Climate change is a development challenge, particularly for developing countries like Cameroon, characterised by heightened vulnerability due to socio-economic structures and reliance on climate-sensitive sectors. Mitigation and adaptation measures are imperative, making financing mechanisms a central focus of international cooperation. This review examines the intricate landscape of climate change financing in Cameroon, providing an in-depth understanding of existing mechanisms, their effectiveness, and the challenges hampering optimal implementation. The study scrutinizes diverse funding sources, encompassing international climate funds, bilateral agreements, and national budget allocations. Key challenges, including fragmented approaches, accessibility issues, and alignment with national development goals, are identified. The study underscores the critical importance of coordination, capacity building, and strategic alignment in navigating the complex climate financing landscape. By systematically addressing these challenges, Cameroon can enhance the utilization of climate finance, ensuring tangible benefits for local communities and substantial contributions to sustainable development goals.

Advances, gaps and way forward in provision of climate services over the Greater Horn of Africa

The Greater Horn of Africa is prone to extreme climatic conditions, thus, making climate services increasingly important in supporting decision-making processes across a range of climate sensitive sectors. This study aims to provide a comprehensive review of the recent advances, gaps and challenges in the provision of climate services over the region, for each of the components of the Global Framework for Climate Services. The study explores various milestones that have been achieved toward climate service delivery. The achievements include improvement of station network coverage, and enhancing the capacity of member states to utilize various tools in data analysis and generate routine climate products. The advancement in science, and availability of High-Performance Computing has made it possible for forecast information to be provided from nowcasting to seasonal timescales. Moreover, operationalizing of the objective forecasting method for monthly and seasonal forecasts has made it possible to translate tercile forecasts for applications models. Additionally, innovative approaches to user engagement through co-production, communication channels, user-friendly interfaces, and dissemination of climate information have also been developed. Despite the significant progress that has been made in the provision of climate services, there are still many challenges and gaps that need to be overcome in order to ensure that these services are effectively meeting the needs of users. The research of the science underpinning climate variability, capacity building and stakeholder engagement, as well as improved data management and quality control processes are some of the gaps that exist over the region. Additionally, communication and dissemination of climate information, including timely warnings and risk communication, require improvement to reach diverse user groups effectively. Addressing these challenges will require strengthened partnerships, increased investment in capacity building, enhanced collaboration between the climate information producers and stakeholders, and the development of user-friendly climate products. Bridging these gaps will foster greater resilience to climate-related hazards and disasters in the Greater Horn of Africa and support sustainable development in the region.

Community Led Climate Adaptation: A case study of Whykong union, Teknaf Upazila under Coxsbazar district in Bangladesh

The study was conducted in Whykong union, Teknaf upazila under coxsbazar district, an area prone to climate-related vulnerabilities. The primary aims and objectives of the case study to assess the impact of climate change, economic condition and community led adaptation initiatives to the selected study location. Data was collected through self-selected sampling from the total amount of community by using KOBO data collection tools. The study reveals successful community-led adaptation practices, innovative approaches, and the impact of local empowerment on resilience. Females account for 55%, while males account for 45%. According to the data, the most common occupation among the participants was housewife, with a frequency of accounting for 54.84% of the total. 54.84% had an income level of less than 5000 BDT. The most prevalent challenge reported by the respondents is the Rohingya Crisis, with a frequency of 93.55%. Poverty is another major challenge, with a frequency of 70.97%. Water scarcity is also a significant challenge, reported by 67.74%. Respondent perception showed that the government which accounts for 74.19% of the total initiatives. The need for climate education and awareness is also recognized, accounting for 70.97% of the total initiatives. This highlights the importance of educating and creating awareness about climate change among the public. CBOs/NGOs/INGOs activities have also contributed significantly, with accounting for 70.97% of the total. Domestic rainwater harvesting accounts for 45% of the community-led initiatives. This initiative involves capturing and storing rainwater for domestic use. It helps to minimize water scarcity during dry periods and reduces reliance on groundwater sources. Pond Sand Filter (PSF) accounts for 30% of the community-led initiatives. It aims to protect the community from climate-related hazards. Diversification of livelihoods accounts for 65% of the community-led initiatives. It helps reduce dependence on climate-sensitive sectors and promotes sustainable livelihood practices. Community participation and decision-making account for 49% of the community-led initiatives. The insights derived from the case study have implications for policymakers, practitioners, and researchers involved in climate resilience efforts. Community-led adaptation in building climate resilience and fostering collaboration and integrating local knowledge into broader climate change mitigation and adaptation.

Sustainable Banking: A Literature Review and Bibliometric Analysis

Sustainable banking is a banking model that integrates environmental, social and governance (ESG) criteria into its operations and decision-making processes. It is focused on promoting sustainable development by considering the long-term impact of its activities on society, the economy and the environment. During the beginning of 80’s, the extensive literatures on sustainable banking were unproductive and this issue continues to the end of 2000’s. Based on this situation, the researchers used Bibliometric analysis of various literatures of about 719 studies which includes articles, reviews, conference papers, book chapters from Scopus and Web of Science (WoS) categories during the period 2000–2021. About 2324 studies were identified from the database. To undergo the research, we acknowledged the main domains which are closely related to sustainable banking from its evolution. The main domains such as corporate social responsibility, banking sector, economic growth, ethical banking, financial stability, green banking, sustainable finance, banking industry, banking system, micro finance and banking regulations. Further we mapped with this into VOS Viewer and analysed how these domains can contribute to sustainability in the banking sector. It is found that, several climatic risks are faced by the banking sector all over the world and hence, the financial sector must make necessary alterations to environmentally friendly strategies to climate sensitive sectors. Sustainability is still under a question mark in the banking sector as several advancements are being introduced not only in technology but also in physical appearance day by day. Finally, to say, the banks must engage in green sustainable strategies towards sustainable banking in the banking industry.

The added value of the process in climate services co-production: Lessons from Niger

Climate services are recognized as an essential tool for sustainable development in strategic and climate-sensitive sectors. In developing countries, particularly in Africa, the literature offers successful examples of application, especially in the agricultural sector, which is dramatically sensitive to climate variability and change. While, initially, particular emphasis was placed on the outcomes of these services and the benefits they provide to users, several authors, more recently, have focused their attention on the process. A climate service is understood as a cyclical process in which the different actors interact, exchange knowledge, and establish relationships and mutual trust. This co-production process serves primarily to bridge the gap between science and society and is particularly appropriate for developing countries. Several authors claim the benefit of the co-production process improving the service usability, but rarely the intrinsic value of the process is recognized. This study aims to describe the process and document its added value using the example of Niger as a case study, where two services addressing drought and floods have recently been developed, tested, and operationalized. This experience allows for inferring lessons that can be useful for researchers and practitioners in developed and developing contexts. The value of the collaboration between different disciplines (transdisciplinarity) and roles (complementarity), and the iterative and interactive learning process emerge as key elements allowing a continuous improvement of the services and the strengthened relationship among actors. The results of this process, albeit qualitatively described in the paper, could guide researchers and practitioners in adopting such an approach and could represent a tangible example for funders and policymakers of the process’s added value. Nevertheless, the article recognizes the need to develop a methodological framework for quali/quantitatively assessing the added value of the co-production process and suggests four dimensions to be considered in further research. Finally, the paper recommends the capitalization of pilot experiences through the national and global frameworks for climate services.

Climate Changes through Data Science: Understanding and Mitigating Environmental Crisis

Climate change represents an urgent environmental crisis with far-reaching risks to ecosystems and human communities worldwide. Rapid development of mitigation strategies and solutions is imperative but relies profoundly on advancements in detection, attribution, and prediction derived from climate data analytics. This paper examines the growing role of data science in not only quantifying anthropogenic climate change but also informing impact assessment and targeted intervention across climate-sensitive sectors. First, we survey established and emerging techniques for climate characterization, including machine learning applications on Earth systems data. Next, we discuss how sophisticated climate models alongside statistical analysis of multi-domain datasets—from migration patterns to crop yields—deepens scientific comprehension of climate change repercussions. Building on these insights, we spotlight data-enabled solution paradigms enabling smart climate action, ranging from high-resolution climate risk mapping, emissions reductions via optimized renewable energy infrastructure, to global warming suppression via solar radiation management. However, we also carefully examine the practical limitations hindering deployment and the ethical concerns posed by certain climate intervention proposals. Ultimately, while data science delivers powerful tools for climate change detection, attribution, and response, this paper underscores how continued climate data gathering alongside cross-disciplinary collaboration is vital to overcome analytical uncertainties, implementation barriers, and moral objections as we work to avert profound environmental breakdown.

Evaluating the Efficiency and Feasibility of Renewable Energy Technologies in Combating Climate Change in Africa

This study examines the efficacy and viability of renewable energy technologies in addressing climate change in Africa. The research utilizes a thorough examination of literature, including academic journals, reports, and policy documents, to provide an enhanced understanding of the effectiveness and practicality of renewable energy technologies in mitigating climate change in Africa. The study finds that Africa has significant untapped renewable energy potential that can contribute to sustainable development and expand access to electricity. It evaluates the efficiency of various renewable energy technologies, such as photovoltaics, wind turbines, hydroelectric power plants, and geothermal systems, considering factors such as energy conversion efficiency, scalability, and reliability. Economic considerations, including investment costs, regulatory frameworks, and development pathways, are also analyzed to determine the feasibility and economic viability of renewable energy technologies. The study emphasizes the challenges posed by climate change in Africa, particularly in relation to energy access disparities and the vulnerability of climate-sensitive sectors like agriculture. Based on the findings, the study recommends strengthening renewable energy policies and regulations, enhancing research and development, promoting public-private partnerships, prioritizing off-grid solutions, focusing on climate change adaptation and resilience, and investing in capacity building and skills

Unraveling the dynamics of climate: empirical evidence from the Indian state of West Bengal

Abstract Understanding climate variability and trends is crucial for managing a host of sectors. Everything from water availability to agricultural productivity is affected by variability and trends in temperature, rainfall, evapotranspiration, and solar radiation. Nevertheless, their dynamics have seldom been explored together, especially in India. To address this gap, the present study investigates the variability, trend, and magnitude of those parameters individually and concurrently using fractal dimension and non-parametric statistics over the Indian state of West Bengal from 1951 to 2020. The results show a south–north gradient in overall climate variability. The Gangetic West Bengal (GWB) is experiencing higher variability, along with a rising minimum temperature (≥0.008 °C year−1) and declining rainfall (≥− 1 mm year−1). Though the Sub-Himalayan West Bengal as a whole shows less variability, its foothills reveal modest variation coupled with increasing maximum temperature (≥0.005 °C year−1), reference evapotranspiration (≥0.4 mm year−1), and decreasing rainfall in the post-monsoon and winter seasons. Based on the results, we identified the western GWB, the Sundarbans, and the sub-Himalayan foothills as the most vulnerable areas and recommended proactive crop and water management strategies. Finally, we underline the need to analyze climate dynamics holistically to manage climate-sensitive sectors efficiently and sustainably.

Co-movements between heterogeneous crude oil and food markets: Does temperature change really matter?

In the global context of temperature change, two climate-sensitive sectors, food and crude oil are directly and severely exposed to frequent climate issues, and their risks are often considered in isolation. However, crude oil and food are closely interrelated. Here, this paper constructs a unified framework to comprehensively explore the impact of temperature change on spillovers between heterogeneous crude oil shocks and corn, soybean, and wheat markets from both time-frequency and quantile perspectives. The bidirectional spillover between oil and food is asymmetric and predominantly positive. Temperature changes generally have an amplifying effect on spillovers, but the effect in extreme cross-market spillovers will vary with crude oil shock types. Differentiated policies for heterogeneous crude oil shocks are therefore necessary. Particularly under the crude oil-specific demand shock, temperature changes can counterintuitively and negatively affect extreme two-way oil-soybean spillovers. This research provides new insights into energy and food security issues under temperature changes.

Recent Changes in Hydrometeorological Extremes in the Bilate River Basin of Rift Valley, Ethiopia

The hydroclimatic extremes such as floods and droughts have been causing damage and losses with rising frequency than ever before. The human-induced and internal climate variability create extreme events and local hydrometeorological changes influencing climate-sensitive sectors. This research is aimed at analyzing the recent changes in the hydrometeorological extremes using indices over the Bilate basin in Ethiopia. Mann-Kendall and Sen’s slope estimator were used to examine changes in hydrometeorological extreme indices. The rainy days’ rate of change falls between +10.64 mm in the downstream to −10.67 mm in the upstream north. The wet day rainfall and heavy rainfall day indices were stronger in the basin’s southwest, implying more likely flood events. The consecutive dry days show a rising tendency with more variability, while the consecutive wet days show no trend with less variability. The change point analysis revealed inconsistencies for the majority of the extreme indices. The stations’ average warmest nights and days significantly increased at a rate of 0.0358°C and 0.0320°C per annum, respectively. The coldest nights in most of the stations show a significant and negligible rise in the basin while on the coldest days more than half of the stations declining. The peak flow in the annual and seasonal time series shows a rising trend and a dominant rise in most low flow indices, which possibly flashes downstream flooding. The global and local climate anomalies revealed a weak correlation, but with overlap of wet and drought years. Basin water resource plans may benefit from identified overlap cross of threshold years for improved flood control and drought monitoring.

Perception of Tasar Silkworm Rearers about the Climate Change Impact on the Tasar Silk Production of Maharashtra

Tasar sericulture is one of the allied sectors of agriculture which is a climate sensitive sector. Understanding farmer’s perception about the climate change impact on the rearing performance tasar silkworm is a key to the development of new climate tolerant technologies so as to encourage the other farmers for adopting sericulture eventually to boost the production of the tasar silk production in the state. Thus, the study was designed to establish farmer’s perception on climate change and its impacts on tasar silkworm rearing performance. The study was conducted in all four tasar silkworm rearing districts of Maharashtra namely Bhandara, Gondia, Gadchiroli and Chandrapur. A total 160 tasar silkworm rearers were selected randomly from all the four districts. Information was collected from each tasar silkworm rearers through Rapid Rural Appraisal (RRA) and focus group discussions (FGD). Climate Change Perception Index (CCPI) and Climate Change Impact Perception Index (CCIPI) were used to assess the perception about the impact of climate change on tasar silk production. Weighted Average Index (WAI) was used to analysis the impact. Results showed that majority of the respondents perceived that the yield and production of tasar cocoons production has reduced compared to last ten years. Moreover, frequent loss of the crop is noticed due to sudden change in the climatic conditions during the rearing of silkworm. Similarly, the high incidence of diseases and pest is also noticed thereby affecting the income generation of the tribal tasar farmers of the state. The results of the study will be useful for formulating appropriate policy against the ill-effects of the climate change. The results will also give impetus to the researcher to develop the thermo tolerant and climate change tolerant or resistant varieties or races of tasar silkworm so as to boost the tasar silk production of the state.

Downscaling long lead time daily rainfall ensemble forecasts through deep learning

Skilful and localised daily weather forecasts for upcoming seasons are desired by climate-sensitive sectors. Various General circulation models routinely provide such long lead time ensemble forecasts, also known as seasonal climate forecasts (SCF), but require downscaling techniques to enhance their skills from historical observations. Traditional downscaling techniques, like quantile mapping (QM), learn empirical relationships from pre-engineered predictors. Deep-learning-based downscaling techniques automatically generate and select predictors but almost all of them focus on simplified situations where low-resolution images match well with high-resolution ones, which is not the case in ensemble forecasts. To downscale ensemble rainfall forecasts, we take a two-step procedure. We first choose a suitable deep learning model, very deep super-resolution (VDSR), from several outstanding candidates, based on an ensemble forecast skill metric, continuous ranked probability score (CRPS). Secondly, via incorporating other climate variables as extra input, we develop and finalise a very deep statistical downscaling (VDSD) model based on CRPS. Both VDSR and VDSD are tested on downscaling 60 km rainfall forecasts from the Australian Community Climate and Earth-System Simulator Seasonal model version 1 (ACCESS-S1) to 12 km with lead times up to 217 days. Leave-one-year-out testing results illustrate that VDSD has normally higher forecast accuracy and skill, measured by mean absolute error and CRPS respectively, than VDSR and QM. VDSD substantially improves ACCESS-S1 raw forecasts but does not always outperform climatology, a benchmark for SCFs. Many more research efforts are required on downscaling and climate modelling for skilful SCFs.

Copernicus global crop productivity indicators: An evaluation based on regionally reported yields

Climate variability places significant pressure on climate-sensitive sectors such as agriculture. Information on crop-related climate and water indicators under climate change becomes essential. The Copernicus Climate Change Service (C3S) provides reliable information about climate change based on climate models, Earth Observations, and in situ data to support adaptation policies. Within this C3S context, the Global Agriculture Sectoral Information System (SIS) has developed a new product based on global crop productivity indicators to document the effects of climate variability on crop yield variability. This study focuses on the evaluation of climate-enhanced, earth observation (EO) based crop productivity indicators i.e., Total Weight Storage Organs (TWSO, equivalent to yield), Total Above Ground Production (TAGP). These variables provide insights into the Spatio-temporal variability of yield, productivity, and development of four major staple crops (rice, maize, soybean, and wheat) for their main production regions at the global scale for the period 2000–2018. In this study, the evaluation was carried out spatially and temporally for the United States of America, India, and China for the period 2000–2018, using reported yield statistics aggregated to the lowest available administrative level of each region. For almost all the crops in the three countries, the skilled error can be reduced by more than 25% for both the indicators TAGP and TWSO compared to the trend of reported yield, suggesting considerable performance in assessing interannual yield variability. Results indicated both indicators individually performed well in many important producing areas of the USA, India, and China. However, if we compare both the indicator’s performance in terms of production totals in the USA, India, and China, TAGP contributes ∼27–50% of total crop production in the USA and India, and ∼47–75% in China. While, TWSO contributes ∼24–41% in the USA and India, and 5–65% in China.

Determinants of climate change adaptation strategies in South India: Empirical evidence

The phenomena of climate change pose multifaceted challenges to crop and livestock farming, with severe implications on smallholder farmers' income and livelihoods. Climate change has profound implications (economic, environmental, and social) predominantly on rainfed regions in developing countries like India, where agriculture constitutes the backbone of the economy. In this context, the current study analyzes how farmers perceive climate change in the rainfed ecosystem in India, farmers' adaptation strategies, and their major determinants in addressing climate change. Data were collected from 400 sample farmers in South India. Discriminant and multinomial logit models were employed to identify the adaptation strategies of the farmers. It was evident that the factors such as off-farm income, farm income, and farming experience significantly influenced the adaptation strategies for tackling climate change. Furthermore, access to climate change information and literacy level are vital determinants in different climate change adaptation strategies, including crop diversification, integrated farming system, contingency plans for farm operations, and adoption of soil and water conservation techniques. However, the study highlights the increasing role of institutions (government and private) in future to safeguard the interests of farmers by offering a wide range of policy, research, and technology interventions. In a nutshell, R&D focus on climate-resilient agriculture, application of ICTs in agro-advisory services, and creation off-farm employment opportunities for the farmers is crucial to sustaining their livelihoods as these serve as potential mitigation strategies to impart resilience to climate-sensitive sectors like agriculture in rainfed ecosystems in India or any other countries.