Climate Change Initiative Research Articles

Review of the Sustainability and Climate Change Initiatives Within the Line of Investment Portfolios

La intensidad y frecuencia de los desastres climáticos vienen aumentado drásti­camente durante la última década, con una tendencia que parece va a continuar en ascenso. Los costos asociados a estos eventos también se incrementan cada vez más. Además, se asume que este tipo de desastres climáticos están estre­chamente relacionados con el cambio climático al que nos enfrentamos como humanidad. En este escenario, el sector financiero juega un papel clave en la forma en que gestiona las acciones contra el cambio climático; por lo tanto, cuanto más fuertes sean sus impactos, más rápido deben implementarse estas acciones. Sin embargo, existe un rezago entre la prioridad de la necesidad de acciones y las acciones que realmente se están implementando. Se han pre­sentado algunos estándares de riesgo, pero aún existe una notable dificultad en comprender y modelar la incertidumbre de los impactos futuros del cambio climático. En este contexto, se necesitan otros tipos de esfuerzos, desde una perspectiva multidisciplinaria, que busquen complementar las acciones contra el cambio climático en el ámbito del sector financiero y las inversiones. En este trabajo se presenta un resumen de las principales iniciativas existentes, pero se destaca cómo, aunque responden a esfuerzos importantes y significativos a nivel corporativo, estas son de naturaleza aspiracional y voluntaria y, por lo tanto, insuficientes para abordar la necesidad mencionada. En este sentido, el artículo busca avanzar hacia la justificación de la construcción de carteras de inversión con principios de sostenibilidad ambiental.

Geo-demographics and source of information as determinants of climate change consciousness among citizens in African countries

Climate change constitutes one of the greatest threats to human health globally and there have been increasing interests in understanding the dynamics of climate change consciousness particularly in less industrialised countries of Africa. Research on cross-country, sub-regional and continent differences in climate change consciousness are rare especially in sub-Saharan Africa. Thus, to complement the existing body of literature, this study was conducted on cross-national predictors of public climate change consciousness. Data from the Afrobarometer round 7 for thirty-four [34] African countries, collected between 2017 and 2020, were used to investigate the influence of geographical, socio-demographics, and source of information on public consciousness of climate change. Statistical analyses of t-test, ANOVA and multiple regression were conducted to test the formulated hypotheses. Results showed a low level of climate change consciousness (CCC) among participants. Radio news was the major source of information for the sampled African citizens. There were significant differences in the CCC of male and female as well as between urban and rural respondents. Significant differences were also found among the sub-regions, educational levels, age groups and occupations. Sources of information and demographic variables significantly influenced the level of CCC among participating citizens. Educational attainment was the single most potent predictor of climate change consciousness. The study recommends that African citizens need capacity building on climate change awareness and initiatives which would assist in mitigating the effects of climate change.

The Practice of International Investment Agreements in the Context of Climate Change Conflicts and Solutions

Climate change is an issue of growing importance on a global scale. The traditional International Investment Agreements (IIAs) and the provisions made in response to climate change often conflict with one another. This has led to a need to find a way to reconcile these differences and establish a complementary and mutually reinforcing relationship between IIAs and climate change treaties. This paper aims to identify the focal points of conflict and explore potential solutions through case analysis and other methods. One potential solution is to make reasonable adjustments to the IIAs to align them with the specific provisions needed to address climate change. By doing so, it may be possible to bridge the gap between traditional IIAs and the requirements of climate change treaties. This approach is essential in ensuring that investment agreements do not hinder the progress of climate change initiatives. It is crucial to find a balance that allows for sustainable economic development while also addressing the pressing concerns of climate change. Finding a way to reconcile the conflicts between traditional IIAs and climate change treaties is of paramount importance. By making targeted adjustments and seeking common ground, it is possible to establish a framework that supports both economic growth and environmental sustainability.

Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977

Abstract. The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km2, respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at https://doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).

Funding Climate Change Initiatives: Utilising the Law for Enhancing Financial Management in Cities

Climate change affects cities disproportionately, and some cities have limited fiscal capacity to address climate change. It is therefore necessary to ensure that the climate funds cities do have at their disposal are used in a way that maximises their impact. However, financial mismanagement and corruption have led to significant money losses in climate funds. Although measures against general public sector corruption can be used to improve climate finance management, the climate finance market poses some unique challenges. Traditional anti-corruption measures can be rethought to maintain their effectiveness against corruption in climate finance. Against this background, the article explores how international and regional law can be used by cities to improve the management of climate funds. Eight financial controls are identified and critically discussed to show how they can be effective in the specific case of climate finance. The article also shows some examples of where these financial controls have been implemented in cities

Using bi-temporal ALS and NFI-based time-series data to account for large-scale aboveground carbon dynamics: the showcase of mediterranean forests

ABSTRACT New remote-sensed biomass change products will transform our capacity to monitor and validate large-scale carbon dynamic in the next decade. In this study, we evaluated the use of multitemporal Airborne Laser Scanning (ALS) and the Climate Change Initiative (CCI) BIOMASS spaceborne mission to estimate AGB dynamics in different Mediterranean forest over an 8-year period (2010–2018). To do so, we evaluated different maps to estimate change in AGB, specifically indirect approach using forest-type specific ALS-based AGB maps using i) countrywide ALS coverage at 25 m resolution (2010–2018) and ii) the global, 100-m resolution CCI maps version 3 (2010–2018). The change in AGB (ΔAGB) was mapped across the study region to compute dynamics by forest type. Our results suggest that the indirect approach using ALS-model-based produced more accurate estimates in change of AGB than CCI when we compared with the design-based AGB estimation using Spanish National Forest Inventory (SNFI) at strata level. The spatial representation of the AGB change indicated that ΔAGB-ALS changes by forest type had an overall gain in biomass at regional level. The ΔAGB total and net annual changes by year and area (ΔAGB, Mg ha−1 year−1) were closed to the values obtained using SNFI at strata level. This study demonstrates the feasibility of enhancing carbon sequestration and stock capacity in Mediterranean forest using multitemporal ALS data and the limitations of global AGB maps at Regional Scale.

Identifying the Spatial Heterogeneity and Driving Factors of Satellite-Based and Hydrologically Modeled Profile Soil Moisture

Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C0 + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields.

Enhancing Sustainable Banking Practices: Implementing the Besgi Framework to Indonesian Bank

Climate change, a global issue largely caused by human activities, is now beginning to be addressed by the G20, including financial institutions. Indonesia, as part of the G20, is implementing a sustainable finance program to improve the financing, durability, and competitiveness of financial services institutions. This study evaluates the adoption of sustainable banking practices in Indonesia within the context of global climate change initiatives. Using the Banks’ Environmental, Social, Governance, and Indirect Impact (BESGI) framework, which provides a comprehensive assessment of banks’ ESG performance using the Multidimensional Synthesis of Indicators (MSI) aggregation method. The BESGI performance of 14 Indonesian banks from 2020-2022 was assessed, revealing varying results of fluctuating data with Mandiri scoring the highest in year 2021 and BTN the lowest in year 2020. The findings indicate a growing emphasis on sustainable finance within the Indonesian banking sector in terms of financing and investment. The BESGI Score has insignificant results on banks’ performance and stability. However, further research is essential to comprehend the implications of these practices on the performance and stability of banks.

Environmental Factors and Adoption of Green Supply Chain Management among SMEs in Nigeria: Moderating Role of Environmental Uncertainty

Environmental regulations and policies have been recognised as significant factors influencing the adoption of Green Supply Chain Management. However, the importance of these factors in influencing sustainable practices within supply chains has been emphasized in recent research as global environmental challenges continue to worsen. As a result, adopting Green Supply Chain Management practices is crucial for ensuring sustainable supply chain operations in Nigeria, as SMEs significantly contribute to economic growth and development in the country. Therefore, this study examines the effect of environmental factors on the adoption of GSCM in the Nigerian SMEs. To measure all the variables, validated items were adapted from prior studies. Thus, 412 copies of questionnaires were retrieved from the selected managers/owners of SMEs after testing for the validity and reliability of instruments through a pilot study. The findings of the study indicated that the environmental factor is a very good predictive faction for the GSCM adoption of SMEs business in Nigeria, most especially in the area of customer demand, environmental regulation, environmental uncertainty, and supplier relationships. Furthermore, the introduction of environmental uncertainty as a moderating effect influence the relationship between an environmental factor and the adoption of GSCM practices in the area of environmental regulation and supplier relationship. The study findings are useful for decision-makers in the SMEs sector so they may build methods to enhance the adoption of GSCM. These findings are also useful for academicians’ future research endeavors. Managers can use those environmental factors concretely as a reference for the companies that intend to support the United Nation SDG-2030 agenda and to find new business opportunities for the implementation of sustainable development. The findings have a number of managerial implications that could contribute to SMEs for planning and development a GSCM strategy through the internal of the green supply chain perspective. This study's recommendations can help Nigeria's SME sector achieve its sustainable development goals and lead global climate change and environmental protection initiatives.

The impact of climate change and sustainability initiatives on forensic practice

The climate change crisis affects all aspects of our lives, and this includes national and global scientific endeavours. The forensic sciences are no different but are yet to engage meaningfully with this agenda or to consider what it means for future practice. This paper explores and discusses a range of impacts on forensic and crime scene practice derived from climate change and proposes the potential implications. The paper concludes by laying out a way forward and a programme of activity to support the forensic sciences to manage the implications of climate change and related sustainability initiatives on the criminal and medico-legal investigative community.

Inferring vegetation response to drought at multiscale from long-term satellite imagery and meteorological data in Afghanistan

Drought caused by climate change has significantly increased vegetation vulnerability in Afghanistan during the last decades. This paper investigates vegetation response to drought at multiple scales across the country based on historical data from 1980 to 2020. It explores the multiscale relationships between drought as indicated by the grid-based standardised precipitation evapotranspiration index (SPEI) and vegetation condition as represented by the satellite-derived vegetation anomaly index (VAI). It also examines the links of dominant land cover with drought and their implications for agriculture. We assess the spatiotemporal correlations by integrating TerraClimate grids with timeseries data sourced from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer) images and ESA CCI (European Space Agency Climate Change Initiative) land cover maps. We evaluated the effect of cumulative drought on predominant land covers. Our results show years 2000–2001 and 2017–2018 as the driest in the last four decades, and vegetation substantially correlated to drought with spatial and temporal variations across Afghanistan. The most sensitive vegetation response months are June to July and the most significant cumulative drought impacts on vegetation are 6 to 8 months. The predominant land covers more prone to negative effects under severe drought are (in order) shrubland, rainfed cropland, grassland, trees, and irrigated cropland. These findings suggest the sensitivity of vegetation to drought that future national scale drought management should be focused on. This study also demonstrates the usefulness of open-access data to researchers and planners in data-poor countries.

The Role of Artificial Intelligence in Fostering Climate Change Initiatives

Climate change has emerged as a preeminent and contested issue on the global stage, sparking intense debate among Non-Governmental Organizations (NGOs), governments and climate activists regarding the most effective approaches to mitigating and adapting to its impacts. An intricate web of interconnected factors, including technological advancements, human activities like agriculture, mining, and fossil fuel use, and the forces of globalization, has indelibly altered ecosystems, human societies, and the global economy. [1] The 28th Conference of the Parties (COP28) to the UNFCCC, held in Dubai in 2023 adopted a roadmap for “transitioning away from fossil fuels,” marking a significant step towards decarbonization. Artificial intelligence (AI) emerges as a promising contender, offering its analytical prowess and data-driven insights to empower more impactful climate action. By harnessing AI’s capabilities across diverse sectors, from environmental monitoring and renewable energy development to resource management and disaster preparedness, scientists and world leaders can craft a comprehensive and forward-thinking plan.

Estimating the Impact of Adaptive Capacity on Vulnerability in Nepal

The adaptive capacity of households is an effective instrument for minimizing climate change-induced vulnerability. Varying adaptive capacity across different income and socio-strata of households make different vulnerability impacts. This paper assesses the impacts of adaptation capacity on household susceptibility index and ranks in Nepal employing an explorative method. In the explorative method, the household vulnerability index as a composite index and multiple regression model are analytical methods. The data set is collected from the questionnaire survey of 642 households in the Sotkhola Water Basin Gadhi, Lekhagaon, and Kunathari through a regression model. As a result, a flood disaster is terrible more than a landslide. By cluster, its intensity is deeper in Gadhi than in Kunathari and Lekhagaon in terms of destruction of assets and non-assets. Total income loss magnifies household vulnerability 7 times deeper. The distribution of vulnerability level falls to poor households three times more than rich households in accordance with headcount poverty and food sufficiency poverty measures. The composite index shows adaptive capacity plays a key role in household vulnerability. Therefore, adaptive capacity is an instrument of resilience to household safety and welfare. In the context of poverty alleviation, preparedness, and climate change initiatives, this result will be valuable to improve the adaptive capacity of the socioeconomically vulnerable population for disaster and climate resilience, further development, and future disaster risk management policy implications.

Nonlinear effects of surface soil moisture changes on vegetation greenness over the Tibetan plateau

As an essential link between terrestrial and climatic ecosystems, vegetation has been altered by the soil hydrological environment associated with frozen soil thaw. However, it is not clear whether fluctuating soil moisture (SM) within the frozen soil zone alters the hydrologic environment to alleviate water stress in plants further, and there are scant previous studies at large scale on whether there is a threshold for SM on vegetation greening. This study integrated SM monitoring data at 125 stations from existing studies, then quantified the advantages of six remote sensing/reanalysis SM products: QTP-DNN-Sm, Global Land Data Assimilation System (GLDAS-Noah), European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land), European Space Agency Climate Change Initiative (ESA CCI), Global-SM, and QTP-SM. Moreover, we assessed the influence of single and multiple regional environmental elements (temperature, precipitation, land surface temperature (LST), normalized difference vegetation index (NDVI), and snow) on SM, as well as identified four trends of SM and vegetation growth for 51.26% of the Tibetan Plateau (TP). The results are as follows: 1) The overall performance of QTP-DNN-Sm products was slightly better than that of Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, and ERA5-Land, with higher median Pearson correlation coefficient (R) value (0.685, 0.686, 0.699, 0.704, 0.300, and 0.582 for QTP-DNN-Sm, Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, and ERA5-Land, respectively) and lowest median unbiased Root Mean Square Error (ubRMSE) (0.061, 0.064, 0.068, 0.064, 0.042, 0.076, and 0.047 m3/m3 for QTP-DNN-Sm, Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, ERA5-Land, and ESA CCI, respectively). 2) NDVI in the frozen soil zone was the best variable to explain SM based on the GeoDetector-based factor detection, and interaction detection results indicated that the interaction between NDVI and temperature was gradually emerging to explain SM from permafrost zones to seasonally frozen ground zones. 3) The nonlinear relationship function between SM and NDVI showed that vegetation growth in 47.76% of the area (mainly distributed in the Changjiang River, Yarlung Zangbo River, and Yellow River basins) was more influenced by phenology. Thresholds existed in 3.49% of TP, where the cumulative effect of SM affects vegetation growth. In 0.65% of the regions, vegetation growth experienced eco-physiological processes of positive relief of water stress and physical processes of negative damage. The ease with which SM altered vegetation growth trends was consistent with the degradation degree of frozen soil type. Although the percentage of regions where the thresholds exist is relatively small, the positive/negative effects of the complex localized intersection between SM and vegetation in these regions could threaten the balance and stability of fragile alpine ecosystems sustained by permafrost.

GETTING OLDER ADULTS INVOLVED IN CLIMATE CHANGE INITIATIVES: REFRAMING AGING AS A POTENTIAL APPROACH

Abstract In the context of climate change, older adults are often solely portrayed in terms of vulnerability and victimization. Although older adults’ vulnerability to the growing effects of climate change and natural disasters is an important topic, this presentation focuses on how to get older adults involved in climate change initiatives and as advocates for the adoption of climate-friendly behavior. The presentation argues that getting the growing population of older adults actively involved in issues of climate change requires moving away from a deficit- and loss-focused view of later adulthood. Instead, a new narrative about aging needs to be adopted, recognizing the many contributions older adults can make to addressing the global climate crisis. These contributions arise out of concerns for the health and well-being of the next generations and draw on older adults’ legacy thinking, lifelong experiences, professional expertise, and ability to work in multigenerational contexts. Such a new narrative recognizes and supports older adults as a “natural resource” and important “human capital.” The presentation will conclude with a discussion of the challenges to get older adults involved in climate change initiatives and with a description of several exemplary programs that have already generated foundational knowledge in this domain.

Why do companies participate in climate change‐related initiatives? Theoretical and empirical analyses of motivations for Japanese companies

AbstractThe 2015 Paris Agreement accelerated the trend toward international decarbonization. Climate change initiatives, such as Science Based Targets, Renewable Energy 100%, and Task Force on Climate‐related Financial Disclosure are rapidly gaining attention as mechanisms that encourage voluntary efforts by companies to limit emissions. However, participation in these initiatives creates a conflict with the pursuit of short‐term profits. Understanding the firms' motivation for initiative participation contributes to the realization of sustainable development. This study empirically examines the motivations for large‐scale Japanese firms to participate in climate change initiatives. For this purpose, first, we update the conventional model of corporate motivations for taking environmental actions to fit the contemporary context. We then quantify individual firms' motivations based on various data and conduct regression analyses to identify the factors contributing to the firms' participation in the initiatives. The results indicate that firms have competitive and legitimate motives to participate in these initiatives.

Gap‐Filled Multivariate Observations of Global Land–Climate Interactions

AbstractThe volume of Earth system observations has grown massively in recent decades. However, multivariate or multisource analyses at the interface of atmosphere and land are still hampered by the sparsity of ground measurements and the abundance of missing values in satellite observations. This can hinder robust multivariate analysis and introduce biases in trends. Nevertheless, gap‐filling is often done univariately, which can obscure physical dependencies. Here, we apply the new multivariate gap‐filling framework CLIMate data gapFILL (CLIMFILL). CLIMFILL combines state‐of‐the‐art spatial interpolation with an iterative approach accounting for dependencies across multiple incomplete variables. CLIMFILL is applied to a set of remotely sensed and in situ observations over land that are central to observing land–atmosphere interactions and extreme events. The resulting gridded monthly time series covers 1995–2020 globally with gap‐free maps of nine variables: surface layer soil moisture from European Space Agency (ESA)‐Climate Change Initiative (CCI), land surface temperature and diurnal temperature range from Moderate‐resolution Imaging Spectroradiometer, precipitation from GPM, terrestrial water storage from GRACE, ESA‐CCI burned area, and snow cover fraction as well as 2‐m temperature and precipitation from CRU. Time series of anomalies are reconstructed better compared to state‐of‐the‐art interpolation. The gap‐filled data set shows high correlations with ERA5‐Land, and soil moisture estimates compare favorably to in situ observations from the International Soil Moisture Network. Soil moisture drying trends in ESA‐CCI only agree with the reanalysis product ERA5‐Land trends after gap‐filling. We furthermore showcase that key features of droughts and heatwaves in major fire seasons are well represented. The data set can serve as a step toward the fusion of multivariate multisource observations.

Bank climate change initiatives, ownership structures, and corporate governance mechanisms: Evidence from emerging economies

AbstractMotivated by the growing attention on climate change acceleration, we examine the interrelationships among corporate governance disclosure index, bank ownership structures, and bank climate change initiatives through the lens of a multi‐theoretical framework. We conduct a fixed‐effects and dynamic two‐step system generalized method of moments models over an extensive dataset. Based on panel data of 220 banks (2,785 observations) from 16 Sub‐Saharan Africa emerging economies between 2007 and 2022, the study observes that banks with higher levels of corporate governance disclosure index engage in more climate change initiatives. The study shows that ownership by institutional and foreign investors is associated with more bank climate change initiatives, while government ownership reduces climate change initiatives. Further, we document that director ownership has no impact on climate change initiatives. The study documents that the association between bank ownership structures and bank climate change initiatives is positively moderated by the extent of the corporate governance disclosure index. This moderating impact improves for banks with high corporate governance mechanisms. Finally, we show that the bank ownership structures‐climate change initiatives linkage and the moderating effect of corporate governance mechanisms on this nexus vary significantly across banks' operating periods. We identify corporate governance disclosure as the potential channel through which bank ownership structures and climate change initiatives are interlinked. Our findings call for banks to adopt and implement good governance disclosure to improve climate change initiatives. The findings make significant theoretical and regulatory contributions.

The level of climate risk reporting performance and firm characteristics: Evidence from the Saudi Stock Exchange

In recent decades, stakeholders have begun to place a greater emphasis on sustainability-related issues, including climate change. Furthermore, the implementation of climate change initiatives has prompted companies to disclose information regarding their evaluation and handling of climate-related risks and potential benefits. However, there is a lack of existing literature that investigates this issue in less developed markets, particularly in Saudi Arabia, where the capital market is rapidly developing. The objective of this study is to assess the degree of performance in reporting climate risk and investigate potential correlations between climate risk reporting performance and firm characteristics among non-financial firms in Saudi Arabia during the period from 2018 to 2021. To achieve the objectives of the study, a total of 515 firm-year observations were utilized, representing 140 non-financial firms in the context of Saudi Arabia. The study’s findings illustrate that the climate risk reporting performance level has steadily improved in Saudi companies over the years. In addition, the findings reveal that firm size and industry exhibit a positive correlation with climate risk reporting performance. Conversely, firm leverage and profitability do not demonstrate such associations. The results are in line with alternative measures of climate risk reporting performance, as well as when climate risk reporting performance is broken down into the four core elements. Policymakers and market regulators could use these results to promote awareness of the factors that influence climate risk reporting performance and to enhance sustainable practices.

A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001–2020 based on a two-step downscaling method

ABSTRACTLong-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existing satellite SM data are only available at coarse scale (~25 km) and suffer a lot from data gaps due to satellite orbit coverage and snow cover, especially on the QTP. Although substantial efforts have been devoted to downscale SM utilizing multiple soil moisture indices (SMIs) or diverse machine learning (ML) methods, the potentials of different SMIs and ML approaches in SM downscaling on the complex plateau remain unclear, and there is still a necessity to obtain an accurate, long-term, high-resolution and seamless SM data over the QTP. To address this issue, this study generated the long-term, high-accuracy and seamless soil moisture dataset (LHS-SM) over the QTP during 2001–2020 using a two-step downscaling method (first downscaling then merging). Firstly, the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1 km utilizing five ML approaches. Then, a dynamic data merging method that considers spatiotemporal nonstationary error was applied to derive the final LHS-SM data. The performance of fifteen SMIs was also assessed and the optimal indexes for downscaling were identified. Results indicated that the shortwave infrared band-based indices had better performance than the near infrared band-based and energy-based indices. The generated LHS-SM data exhibited satisfying accuracy (mean R = 0.52, ubRMSE = 0.047 m3/m3) and certain improvement to the ESA CCI SM data both at station and network scales. Compared with existing 1 km SM datasets, the LHS-SM data also showed the best performance (mean R = 0.62, ubRMSE = 0.047 m3/m3), while existing datasets either failed to fully characterize the spatial details or had some data gaps and unreasonable distributions. Strong spatial heterogeneity was observed in the SM dynamics during 2001–2020 with the southwest and northeast showing a “dry gets wetter” scheme and the southeast presenting a “wet gets drier” trend. Overall, the LHS-SM dataset gained its added values by compensating the drawbacks of existing 1 km SM products over the QTP and was much valuable for many regional applications.