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Conventional nature conservation has attempted to protect biodiversity, ecological integrity (i.e., composition and biomass/abundance of native species), and ecosystem services in parallel, assuming generally positive relationships among them. However, many native species are potentially weakened by and maladapted to a new, warmer climate. Such native species may not be able to offer ecosystem services at previous levels. If the relationship between ecosystem services and native species is no longer positive due to climate change, pursuing ecological integrity could be ineffective for maximizing ecosystem services. Hence, there is room to investigate future relationships among biodiversity, ecological integrity, and ecosystem services. This investigation can be done by describing the changes in ecosystem services as a function of biodiversity and ecological integrity. Herein, biodiversity and ecological integrity can be expressed by phylogenetic diversity and species intactness indices based on abundance, respectively. According to my hypothetical figure, the metrics of biodiversity and ecological integrity are potentially not positively but rather negatively related to ecosystem services. The hypothetical figure can be examined and improved using three different approaches: computer simulations (e.g., the LANDIS‐II forest simulation model), collection of actual data (e.g., measuring ecosystem services of some sites with psychrophilic and thermophilic species), and questionnaire surveys about cultural services (e.g., using digitally manipulated photographs and generative adversarial networks). Thus, we can successfully enable practitioners to understand new relationships, which will be relevant to ecosystem management under changing climates. [ABSTRACT FROM AUTHOR]

This paper develops the notion of metonymy scenarios by exploring the social and cognitive dimensions of various creative uses of metonymy in a collection of digital banners created for the Global Climate Strike movement. The paper argues that the banners exploit existing metonymic relationships to activate dominant anthropocentric discourses in society, and to subvert them via processes of recontextualisation and reappropriation, in order to challenge system conventions and normative attitudes regarding climate change. The literature to date has not adequately considered metonymy as a dynamic and scenario-activating cognitive operation, nor has it thoroughly investigated the relationship between metonymy and irony. However, the data analysed here show that several creative uses of metonymy, including twice-true metonymy, metonymy in combination with metaphor, and the juxtaposition of different metonymies are markers of what this paper posits as metonymic mininarratives or scenarios. [ABSTRACT FROM AUTHOR]

Climate change is a very debated topic among academia, national and international institutions. Therefore, policies and tools for diminishing pollutant emissions are in place in a number of countries. Among them, taxation and renewable energy use seem to be among the most important. This paper aims at testing the impact of certain tools for climate change policy implementation, such as environmental taxes, renewable energy use, real productivity, employment rate taking into consideration the level of economic development and the GINI coefficient, on the greenhouse gas emissions in two important sectors of the economy: industry and commerce. Panel data analysis is used for a cluster of nine developing countries of the European Union that have the per capita GDP at purchasing power parity lower than 80% of the EU average, during 2008-2021. Similar to other studies, the results show a negative relationship between environmental taxation and greenhouse gas emissions in the industrial sector, and a positive one in the commercial sector. The latter is explained by the fact that transport, which is a main pollutant sector, is also one of the most difficult sectors to achieve green transition, given high associated costs. The analysis also shows that renewable energy use discourages the emissions of greenhouse gases, both in the industrial and commercial sectors, so that fostering investment in renewables is an important factor for addressing climate change and promoting a sustainable growth. [ABSTRACT FROM AUTHOR]

This paper explores some of the emerging impacts of climate change on urban environments, specifically, in this case, on the city of Bucharest. While using 2008 to 2023 data acquired from meteoblue.com, the analysis identifies specific trends in the local climate through two indicators: (i) the number of (air temperature) degree-hours beyond or below different thresholds and (ii) the Temperature-Humidity Index (ITU). The key findings relate to some observations that can be made after processing the data and looking at the different resulting trends. On the one hand, the number of degree-hours above certain temperature thresholds seems on an ascendant trendline. On the other hand, a decreasing trendline for degree-hours below lower specific thresholds can be noticed. This suggests that an increase in energy demand for cooling and a slight decrease in energy demand for heating buildings might be expected in the following decades, something with various consequences (e.g., socio-economic, environmental, etc.). When considering the other parameter assessed, the Temperature-Humidity Index (ITU), again, an escalating trend may be observed throughout the period for which data was available. Collectively, the findings raise a flag on specific increasing trends in levels of outdoor discomfort. Although the study's findings may be preliminary and exploring additional datasets would be desirable, they highlight a possible local warming trend and the need for early-stage urban adaptation strategies to increase local climate resilience. Given the concerns raised, the concept of Blue-Green Infrastructure (BGI) is identified as an essential solution to some of the different issues brought forth while assessing the data mentioned earlier and even when acknowledging the current problems experienced by Bucharest's inhabitants during the hot season. [ABSTRACT FROM AUTHOR]

Achieving the net zero emissions target that was set in the Paris Agreement to mitigate the risks of climate change seems increasingly difficult as countries and sectors of the economy are falling behind the expected trajectory. The building and construction industry, as one of the main contributors to global emissions, has an essential role to play toward this aim. Net zero emissions target has been introduced to this sector as well; however, achieving it is a very challenging and complex task. Many studies have been undertaken on implementing different measures and strategies to reduce the industry's carbon footprint. These studies identified many challenges and barriers in transforming the industry. This paper aims to provide a systematic review of challenges that organisations face in delivering new net zero emissions buildings. The relevant journal articles published since the Paris Agreement were identified and analysed using mixed-method data analysis, including quantitative (science mapping) and qualitative (thematic) analysis. The result showed increased attention to the subject over this period, with China, the UK, and Australia being the top contexts for research. The most discussed groups of barriers were "economic", "knowledge", and "technical", respectively, followed by "organisational", "market", "technological", and "legal" barriers. [ABSTRACT FROM AUTHOR]

While radiocarbon (14C) abundance in standing stocks of soil carbon has been used to evaluate rates of soil carbon turnover on timescales of several years to centuries, soil-respired 14CO2 measurements are an important tool for identifying more immediate responses to disturbance and climate change. Soil 14CO2 data are often temporally sparse, however, and could be interpreted better with more context for typical seasonal ranges and trends. We report on a semi-high-frequency sampling campaign to distinguish physical and biological drivers of soil 14CO2 at a temperate forest site in Northern Wisconsin, USA. We sampled 14CO2 profiles every three weeks during snow-free months through 2012, in three intact plots and one trenched plot that excluded roots. Respired 14CO2 declined through the summer in intact plots, shifting from an older C composition that contained more bomb 14C, to a younger composition more closely resembling present 14C levels in the atmosphere. In the trenched plot respired 14C was variable but remained comparatively higher than in intact plots, reflecting older bomb- enriched 14C sources. Although respired 14CO2 from intact plots correlated with soil moisture, related analyses did not support a clear cause-and-effect relationship with moisture. The initial decrease in 14CO2 from spring to midsummer could be explained by increases in 14C-deplete root respiration; however, 14CO2 continued to decline in late summer after root activity decreased. We also investigated whether soil moisture impacted vertical partitioning of CO2 production, but found this had little effect on respired 14CO2 because CO2 contained modern bomb-C at depth, even in the trenched plot. This surprising result contrasted with decades to centuries-old pre-bomb CO2 produced in lab incubations of the same soils. Our results suggest that root-derived C and other recent C sources had dominant impacts on 14CO2 in situ, even at depth. We propose that 14CO2 may have declined through late summer in intact plots because of continued microbial turnover of root-derived C, following declines in root respiration. Our results agree with other studies showing large seasonal fluctuations in respired Δ14CO2, and suggest root C inputs are an important driver. [ABSTRACT FROM AUTHOR]

Hydroacoustic data document the occurrence of 5 flare clusters and several single flares from which bubbles rise through the entire water column from an active seep site at 40 m water depth in the central North Sea. We investigated the difference in dissolved methane distributions along a 6 km transect crossing this seep site during a period of seasonal summer stratification (July 2013) and a period of well mixed winter water column (January 2014). Dissolved methane accumulated below the seasonal thermocline in summer with a median concentration of 390 nM, whereas during winter, methane concentrations were much lower (median concentration of nM) and punctually elevated due to bubble transport. High resolution methane analysis by an underwater mass-spectrometer confirmed our summer results and were used to document prevailing stratification over the tidal cycle. Although sufficient methane was available, microbial methane oxidation was limited during both seasons. Measured and averaged rate constants (k') using Michaelis Menten kinetics were on the order of 0.01 days-1, equivalent to a turnover time of 100 days. Time series measurements indicated an uptake of only 5-6% of the gas after 4 days, and no known methanotrophs and pmoA-genes were detected. Estimated methane fluxes indicate that horizontal eddy transport rapidly disperses dissolved methane, vertical transport becomes dominant during phases of high wind speeds, and relative to these processes, microbial methane oxidation appears to be comparably low. To bridge the discrete field data we developed a 1-D seasonal model using available yearlong records of wind speed, surface temperature and thermocline depth. The model simulations show a peak release of methane at the beginning of fall when the water column becomes mixed. Consistent with our field data, inclusion of microbial methane oxidation does not change the model results significantly, thus microbial oxidation appears to be not sufficient to notably reduce methane during summer stratification before the peak release in fall. [ABSTRACT FROM AUTHOR]

Conducting a vulnerability and risk assessment (VRA) is a critical step in adaptation planning and implementation. This research paper has assessed how the co-developed approach helped the Government of Nepal and stakeholders in Nepal to agree on and operationalize the vulnerability and risk assessment framework and what works best for the country's context. The methodological framework consists of eight steps including (i) scoping, (ii) reviewing the framework (iii) identifying data sources (iv) exploring data sources, nature, and character (v) data collection, tabulation, filtration, and normalization (vi) weightage and composite value (vii) analysis of the data (viii) identifying climate change impact, vulnerability, and risk. The output of the vulnerability and risk assessment in Nepal presents a compilation of impacts, vulnerabilities, and risks and a description of their context, root causes, and trends. These research-based assessment can be used to draw some possible adaptation options and improved decision-making at the national and sub-national levels. However, there were some challenges faced in analyzing the vulnerability and risks based on the indicators. There was a lack of a multi-year, complete, and uniform database, and difficulties in developing scenarios of hazards due to unclarity on climate change attribution. The lessons from this paper will be important for designing a more practical and country-driven VRA framework and methodology for other countries. [ABSTRACT FROM AUTHOR]

Citizen science is a research practice that relies on public contributions of data. The strong recognition of its educational value combined with the need for novel methods to handle subsequent large and complex data sets raises the question: Is citizen science effective at science? A quantitative assessment of the contributions of citizen science for its core purpose – scientific research – is lacking. We examined the contribution of citizen science to a review paper by ornithologists in which they formulated ten central claims about the impact of climate change on avian migration. Citizen science was never explicitly mentioned in the review article. For each of the claims, these ornithologists scored their opinions about the amount of research effort invested in each claim and how strongly the claim was supported by evidence. This allowed us to also determine whether their trust in claims was, unwittingly or not, related to the degree to which the claims relied primarily on data generated by citizen scientists. We found that papers based on citizen science constituted between 24 and 77% of the references backing each claim, with no evidence of a mistrust of claims that relied heavily on citizen-science data. We reveal that many of these papers may not easily be recognized as drawing upon volunteer contributions, as the search terms “citizen science” and “volunteer” would have overlooked the majority of the studies that back the ten claims about birds and climate change. Our results suggest that the significance of citizen science to global research, an endeavor that is reliant on long-term information at large spatial scales, might be far greater than is readily perceived. To better understand and track the contributions of citizen science in the future, we urge researchers to use the keyword “citizen science” in papers that draw on efforts of non-professionals. [ABSTRACT FROM AUTHOR]

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ > 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences. [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which (a) are of high spatial and temporal resolution, (b) explore a range of future climate change scenarios, (c) are consistent with historical observations in the historical period, and (d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing 11 near-surface meteorological variables that can be used to as input to many different impact models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980–2080. It was derived from the state-of-the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways 2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9–1.9 K for RCP2.6 up to 2.8–4.3 K for RCP8.5 between 1980–2000 and 2060–2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to - 39 %). All four RCP scenarios and ensemble members are also provided with bias correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, an extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: 10.5285/8194b416cbee482b89e0dfbe17c5786c. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world's water supply concerns effectively. [ABSTRACT FROM AUTHOR]

According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change. [ABSTRACT FROM AUTHOR]

Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin‐up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi‐Analytical Spin‐Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin‐up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin‐up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin‐up on site and globally in comparison with other spin‐up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change. Plain Language Summary: Land carbon cycle models, a critical component of earth system models, have been developed to investigate and understand ecological responses to global changes. However, the model spin‐up process to achieve steady state are usually time‐consuming and pose great computational burden. The spin‐up problem hinders our ability to study some key issues in land carbon cycle modeling, such as sensitivity analysis. This paper applies a new SASU framework to accelerate spin‐up of Community Land Model matrix version 5. Compared with traditional native dynamic approach, SASU speeds up the spin‐up by approximately 50 times, achieving the fastest spin‐up speed than all the previously recorded methods. Our SASU method is applicable to biogeochemical models and, therefore, liberates the models for computationally costly studies, such as parameter perturbation ensemble analysis and data assimilation. Key Points: A new semi‐analytical spin‐up (SASU) framework combines the default accelerated spin‐up method and matrix analytical algorithmSASU accelerates CLIM5 spin‐up by tens of times, becoming the fastest method to our knowledgeSASU is applicable to most biogeochemical models and enables computationally costly study, for example, sensitivity analysis [ABSTRACT FROM AUTHOR]

Permaculture is a growing but little researched phenomenon emphasising care for the environment, equity, fair treatment of people and working with—and not against—nature. It thus represents a potential alternative to business as usual, capable of addressing fundamental challenges posed by human‐made climate change. The paper examines a previously ignored site of entrepreneurship by taking a practice perspective, exploring connections between the practice and growth of permaculture and institutional entrepreneurship. It assesses practice‐related and institutional factors affecting the start‐up and operation of permaculture enterprises in the United Kingdom. The study maps and surveys UK Permaculture Association members who have started up their own business and reports on qualitative data from personal interviews with twenty of them. Data analysis employs NVivo software and involves thematic analysis pertaining to the practice, institutional biographies and institutional portfolios of permaculture entrepreneurs. The findings show the importance of permaculture activists' institutional biographies and institutional portfolios to the start‐up and operation of permaculture enterprises and for shaping permaculture‐related practice. The contribution of the paper lies in how it balances attention to individual agency with subfield‐specific, organisational field and macrosocial factors in understanding 'beyond profit' entrepreneurship. [ABSTRACT FROM AUTHOR]

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A green economy is expected to solve the failure of the conventional economy to address climate change. It would do this by emphasising the balance between economic and environmental factors – an approach that is in line with the existing concept of Islamic finance. As a new field, research related to the green economy is still developing. This study attempts to provide quantitative information on this development. In total, Scopus lists 1183 papers, including journal articles, book chapters, and conference papers, on this subject, all produced between 1961 and 2021. This study employed the Biblioshiny package from R-studio and Excel software to synthesise and analyse this data. In addition, we identified relevancies between the green economy and Islamic finance using existing research. Regarding the results obtained, the natural fit between Islamic finance and a green economy would naturally support the implementation of the latter. Furthermore, the results show that sukuk, in particular, is a potential instrument in Islamic finance for promoting a green economy. [ABSTRACT FROM AUTHOR]

The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept. However, the traditional spatio-temporal variogram estimator, which is commonly employed for these purposes, is extremely sensitive to outliers. We approach this problem in two ways in the paper. First, new robust spatio-temporal variogram estimators are introduced, which are defined as M-estimators of an original data transformation. Second, we compare the classical estimate against a robust one, identifying spatio-temporal outliers in this way. To accomplish this, we use a multivariate scale-contaminated normal model to produce reliable approximations for the sample distribution of these new estimators. In addition, we define and study a new class of M-estimators in this paper, including real-world applications, in order to determine whether there are any significant differences in the spatio-temporal variogram between two temporal lags and, if so, whether we can reduce the number of lags considered in the spatio-temporal analysis. [ABSTRACT FROM AUTHOR]

As the climate changes, it is important to understand the effects on the environment. Changes in wildland fire risk are an important example. A stochastic lattice-based wildland fire spread model was proposed by Boychuk et al. (2007), followed by a more realistic variant (Braun andWoolford, 2013). Fitting such a model to data from remotely sensed images could be used to provide accurate fire spread risk maps, but an intermediate step on the path to that goal is to verify the model on data collected under experimentally controlled conditions. This paper presents the analysis of data from small-scale experimental fires that were digitally video-recorded. Data extraction and processing methods and issues are discussed, along with an estimation methodology that uses differential equations for the moments of certain statistics that can be derived from a sequential set of photographs from a fire. The interaction between model variability and raster resolution is discussed and an argument for partial validation of the model is provided. Visual diagnostics show that the model is doing well at capturing the distribution of key statistics recorded during observed fires. [ABSTRACT FROM AUTHOR]

Drought periods can have important impacts on plant productivity and ecosystem functioning, but climatic conditions other than the lack of precipitation during droughts have never been quantified and have therefore not been considered explicitly in both experimental and modeling studies. Here, we identify which climatic characteristics deviate from normal during droughts and how these deviations could affect plant responses. Analysis of 609 years of daily data from nine Western European meteorological datasets reveals that droughts in the studied region are consistently associated with more sunshine (+45%), increased mean (+1.6 °C) and maximum (+2.8 °C) air temperatures and vapour pressure deficits that were 51% higher than under normal conditions. These deviations from normal increase significantly as droughts progress. Using the process-model ORCHIDEE, we simulated droughts consistent with the results of the dataset analysis and compared water and carbon exchange of three different vegetation types during such natural droughts and droughts in which only the precipitation was affected. The comparison revealed contrasting responses: carbon loss was higher under natural drought in grasslands, while increased carbon uptake was found especially in decidious forests. This difference was attributed to better access to water reserves in forest ecosystems which prevented drought stress. This demonstrates that the warmer and sunnier conditions naturally associated with droughts can either improve growth or aggravate drought-related stress, depending on water reserves. As the impacts of including or excluding climatic parameters that correlate with drought are substantial, we propose that both experimental and modeling efforts should take into account other environmental factors than merely precipitation. [ABSTRACT FROM AUTHOR]

The agricultural sector and how it relates to climate change is today emerging as a central subject of debate and critique, because it is heavily impacted by, and at the same time, a primary contributor to, climate change. The intertwined, complex relationship between the sector and climate change is among the unprecedented challenges now facing the European Union (EU). The complexity of the relationship calls for the establishment of a sustainable, future climate-proof, adapted and resilient sector with strong adaptive capacity. This paper argues that over the past decades, strong emphasis has been placed on how to mitigate the negative effects of climate change across the sector, causing it to fall behind in terms of adaptation. Although adaptation is now part of the sector's development agenda, sectoral adaptation performance across member states remains low. In order to justify an accelerated adaptation process across the sector, the paper develops a Relative Climate Change Adaption Index (RCCAI) for the sector based on Eurostat data. The analysis shows that there is no single member state across the EU whose agricultural sector can be considered as fully climate-adapted (resilient), and thus validates the hypothesis that adaption efforts must be stepped up across the sector. To ensure continued sectoral adaptive capacity improvement, the paper recommends coherent integration and accelerated implementation of adaptation practices and policies alongside the Common Agricultural Policy (CAP) for the sake of both private and public interests. [ABSTRACT FROM AUTHOR]

The topics of climate change and pollutant emission reduction are dominating societal discussions in many areas. In automotive development, with the introduction of real driving emissions (RDE) testing and the upcoming EU7 legislation, there are endless boundary conditions and potential scenarios that need to be evaluated. In terms of vehicle calibration, this is leading to a strong focus on alternative approaches such as virtual calibration. Due to the flexibility of virtual test environments and the variety of RDE scenarios, the amount of data collected is rapidly increasing. Supporting the calibration engineers in using the available data and identifying relevant information and test scenarios requires efficient approaches to data analysis. This paper therefore discusses the potential of data clustering to support this process. Using a previously developed approach for event detection in emission calibration, a methodology for the automatic categorization of events is presented. Approaches to clustering algorithms (hierarchical, partitioning, and density-based) are discussed and applied to data of interest. Their suitability for different signals is investigated exemplarily, and the relevant inputs are analyzed for their usability in calibration procedures. It is shown which clustering approaches have the potential to be implemented in the vehicle calibration process to provide added value to data evaluation by calibration engineers. [ABSTRACT FROM AUTHOR]

The carbon embodied in buildings is an important proportion of our emissions and needs to be radically reduced in order to support climate change mitigation. The highest proportion of embodied carbon is usually emitted during the product stage, and within the structural elements. Therefore, reducing the carbon embodied in the structural materials is likely to have a major impact. In most buildings, the majority of embodied carbon comes from steel and concrete. But although there are now hundreds of registered Environmental Product Declarations (EPD) for cements and concretes, there has been very limited independent published information comparing the embodied carbon of different concrete mixes and raw materials. This lack of comparative data limits the potential to make appropriate decisions at early design stages leading to low carbon buildings. The authors have recently conducted a review of verified EPD for concrete mixes and for concrete’s key constituents, including cement, identifying the range of carbon coefficients. This paper provides guidance on making use of the coefficient ranges provided in that research: to support the verification of EPD for concrete and its raw materials; in material selection; in assessing building level embodied carbon; in benchmarking; and in the setting of reduction targets. [ABSTRACT FROM AUTHOR]

This paper explores the determinants of carbon taxation by focusing on recent efforts to tax carbon in Washington State. The paper identifies citizen demand, energy interests, and tax structure as possible factors in the emergence of carbon taxation. Washington has relatively high demand for climate action, and is unique with respect to its high reliance on indirect taxation and its low level of local fossil fuel interests. I argue this context, especially the high reliance on indirect taxation, makes taxation more appealing than other carbon pricing alternatives. I then test whether local tax structure shapes preferences for carbon taxation using county level opinion data. I find evidence that support for carbon taxation is higher in states with higher gasoline taxes, but no effect from sales and sin taxes. I conclude summarizing various accounts of each initiative's failure, and highlight a few lessons from the efforts to tax carbon in Washington state. [ABSTRACT FROM AUTHOR]

Given the extreme dependence of agriculture on weather conditions, this paper analyses the effect of climatic variations on this economic sector, by considering both a huge data set and a flexible spatiotemporal model specification. In particular, we study the response of N‐fertilizer application to abnormal weather conditions, while accounting for other relevant control variables. The data set consists of gridded data spanning over 21 years (1993–2013), while the methodological strategy makes use of a spatial dynamic panel data (SDPD) model that accounts for both space and time fixed effects, besides dealing with both space and time dependences. Time‐invariant short‐ and long‐term effects, as well as time‐varying marginal effects are also properly defined, revealing interesting results on the impact of both GDP and weather conditions on fertilizer utilizations. The analysis considers four macroregions—Europe, South America, Southeast Asia and Africa—to allow for comparisons among different socio‐economic societies. In addition to finding both spatial (in the form of knowledge spillover effects) and temporal dependences as well as a good support for the existence of an environmental Kuznets curve for fertilizer application, the paper shows peculiar responses of N‐fertilization to deviations from normal weather conditions of moisture for each selected region, calling for ad hoc policy interventions. [ABSTRACT FROM AUTHOR]

We compiled and analyzed long-term (1961-2005) zooplankton community data in response to environmental variations in Lake Biwa. Environmental data indicate that Lake Biwa had experienced eutrophication (according to total phosphorus concentration) in the late 1960s and recovered to a normal trophic status around 1985, and then exhibited warming since 1990. Total zooplankton abundance showed a significant correlation with total phytoplankton biomass. Following a classic pattern, cladoceran/calanoid and cyclopoid/calanoid abundance ratio was related positively to eutrophication. Zooplankton community exhibited a significant response to the boom and bust of phytoplankton biomass as a consequence of eutrophication-reoligotriphication and warming. Moreover, our analyses suggest that the Lake Biwa ecosystem exhibited a hierarchical response across trophic levels; that is, higher trophic levels may show a more delayed response or no response to eutrophication than lower ones. We tested the hypothesis that phytoplankton community can better explain the variation of zooplankton community than bulk environmental variables, considering that phytoplankton community may directly affects zooplankton succession through predatorprey interactions. Using a variance partition approach, however, we did not find strong evidence to support this hypothesis. We further aggregate zooplankton according to their feeding types (herbivorous, carnivorous, omnivorous, and parasitic) and taxonomic groups, and analyzed the aggregated data. While the pattern remains similar, the results are less clear comparing with the results based on finely resolved data. Our research explored the efficacy of using zooplankton as bio-indicators to environmental changes at various data resolutions. [ABSTRACT FROM AUTHOR]

Climate change is a challenge for all of humanity. Should financial be environmentally responsible? This paper uses the Toda–Yamamoto test and the extended Fourier Toda–Yamamoto test re-examine the relationship between financial development and environmental quality in 31 provinces and municipalities in China during the period 2000–2018. We find that financial development in certain regions has effectively reduced the concentration of PM2.5, which indicates that it has a significantly positive effect on environmental quality, though with regional differences. After considering structural shifts, the relationship between financial development and environmental improvement is found to be significant in more regions, indicating that China has undergone structural shifts in these aspects. The aforementioned conclusions are also supported by further robustness tests. China can consequently utilize the positive impact of finance for advocating carbon emission reduction and improving environment quality, therefore contributing to the response to global climate change. [ABSTRACT FROM AUTHOR]

The present stage of development of artificial intelligence (AI) is based on advanced technologies and modern methods and algorithms of machine learning (ML) including deep machine learning, intellectual data analysis and other fundamental scientific areas, which in turn play an important role in delivering applied solutions in almost all areas of digital economy. However, the corresponding expansion of the AI application range and complication of the class of problems, as well as of the spectrum and the volume of data used for creation of applied AI models and AI-based intelligent system call for a significant expansion of the theoretical and algorithmic basis of AI including development of ML methods that involve mathematical and physical models of objects and phenomena of domains, methods of fusion of multimodal data, methods for constructing geometric and topological components of deep neural networks, methods for modeling 3D objects, etc. In 2021, in order to cope well with these challenges, Skoltech Research Center for Applied Artificial Intelligence was created with support from the Federal Program "Artificial Intelligence." The aims of the Center are as follows: creation of a scientific and technological basis for solution of a wide range of relevant applied tasks for purposes of sustainable development of the economy of the Russian Federation including optimization problems of managerial decisions aimed at reduction of the carbon footprint and other urgent ESG tasks; environmental monitoring tasks for detection of anomalies and extreme situations forecast; assessment of economic and social risks and their dynamics caused by climatic changes; predictive analytic problems, etc. In the present paper, we describe new AI technologies, models, methods and algorithms developed in the Center, describe the main applied areas of research of the Center, and provide a briefly survey of the already developed scientific and applied results. [ABSTRACT FROM AUTHOR]

Here we discuss the accelerating global mean sea level (GMSL) recently proposed by Nerem et al. (2018) in the Proceedings of the National Academy of Sciences (PNAS). As the results for the past cannot be changed continuously to prove that the models are right for the future, this GMSL is more a sort of computation than a true measurement. Thus the claim is quickly dismissed as flawed. What is disturbing, is the impossibility of open discussions in PNAS papers directly contributed by National Academy of Sciences members even if they are inconsistent. [ABSTRACT FROM AUTHOR]

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The events of recent years indicate climate change in nature. Climate change could affect the lifetime of transport infrastructure. Changes in the temperature, humidity, and use of de‐icing material may stimulate the development of damages in road and bridge structures. According to Latvian Environment, Geology, and Meteorology Centre data, average air temperature values have increased by 0.7 °C (1981‐2010 compared to 1961‐1990 g). Road maintenance organizations need information on the actual weather conditions on each road: above the road pavement, on the surface, and below the surface. Sixtyfive meteorological stations on roads throughout Latvia's territory provide this information. This paper analyzes data from six road metrological stations obtained from 2001 to 2021. The results show that over the 20 years period, the average temperature determined nearby asphalt pavement of the road has increased by 1.4 C. This is because the road asphalt pavement heats more, and the temperature measured in its vicinity is higher and should be considered when modeling the development of damage to the transport infrastructure. [ABSTRACT FROM AUTHOR]

This paper investigates the factors driving food security in West African countries. Specifically, it examines the impact of natural resource rents, institutional quality, and climate change on food security while controlling for industrialization and economic growth. Our research is motivated by the urgent need for swift policy action to address the escalating food crisis in the region and prevent any potential catastrophic consequences. Second-generation econometric techniques are utilized for accurate and reliable outcomes based on yearly datasets from West African countries from 2000 to 2020, and the countries are sub-grouped into low-income and lower-middle-income. The findings unveil the panel as heterogeneous and cross-sectionally based, and all the study variables are first differenced stationary and co-integrated in the long run. Hence, the Augmented Mean Group and Common Correlated Effects Mean Group estimators are utilized to explore the relationships between the variables, and the findings reveal that natural resource rents, climate change, and industrialization are detrimental to food security across the sub-groups. However, the outcomes affirm institutional quality and economic growth as beneficial drivers of food security across the sub-groups. Therefore, this study recommends that authorities of both low-income and lower-middle-income countries make substantial investments in sustainable natural resource utilization and also work towards enhancing the efficiency and effectiveness of their institutions, as well as investing in environmental research to explore climate change mitigation possibilities that could enhance food security in West Africa. [ABSTRACT FROM AUTHOR]

Bibliometric analysis is a research method that is used to identify research gaps and the potential paths of empiric exploration of such categories that seemingly have been studied, but which in which enormous scientific and utilitarian capabilities remain undiscovered. Sustainable development is one such category. The purpose of this article is to present the development and evolution of scientific literature on sustainable development, and the research goal is to forecast the developmental directions for this category. Bibliometric analysis was used in the research. Scopus, the international interdisciplinary database, was the data source. Maps of co-occurrence of keywords relating to sustainable development were prepared in VOSviewer ver. 1.6.19. The results of the research provided new and crucial knowledge about the features of contemporary literature on sustainable development, determined following analysis of the content of selected open access journals that are representative for this field. The chronological aspect of research of the concept of sustainable development was also underlined. This, in the context of management development, resulted in essential conclusions and results pointing to the potential directions of development of the analysed area. The results also identify the key publications and journals for the subject being researched. This is one of the few works which features a comprehensive bibliometric analysis of sustainable development (Díaz-López et al., 2021; Nobanee et al., 2021; Yamaguchi et al., 2023; Zhu & Hua, 2017). The added value of the analyses is the information that was prepared in the form of rankings using bibliometric indicators such as the number of citations, number of articles cited, and the map of co-cited journals. [ABSTRACT FROM AUTHOR]

Nonstationary is one of the prominent phenomena in the current hydrological time series due to climate change and urban expansion. In this study, using the long time series rainfall data from rain gauges and satellite rainfall data, the trend and abrupt change of rainfall in the Taihu Lake basin, China, are examined by the Mann–Kendall (MK) test and the Pettitt test, using rain gague data. For seven water conservancy zones in this basin, the intensity–duration–frequency curves (IDFs) are obtained using satellite rainfall and the stochastic storm transposition (SST) method, providing a method for rainfall frequency analysis based on nonstationary assumption. The IDFs results between the conventional frequency analysis method with the stationary assumption and the SST-based method are compared. The results show an overall increasing trend of annual total rainfall in the Taihu Lake Basin, with significant changes at most stations. The SST-based results show a significant difference of IDFs in seven conservancy zones, which are linked to nonstationary changes in rainfall series. Our results provide an important reference for understanding the nonstationary changes and nonstationary frequency analysis of extreme rainfall in the Taihu Lake basin. [ABSTRACT FROM AUTHOR]

This paper aims to study wet-cold compound events (WCCEs) over Greece for the wet and cold season November-April. WCCEs are divided in two different compound events (TX-RR) and (TN10 RR) and two different approaches using fixed (RR over 20 mm/day and Temperature under 0 oC) and percentile (RR over 95th and Temperature under 5th) thresholds. Observational data from the Hellenic National Meteorology Service (HNMS) and simulation data from reanalysis and EUROCORDEX models were used in the study for the historical period 1980-2004. Simulation datasets from projection models were employed for the near future period (2025-2049) to study the impact of climate change on the occurrence of WCCEs under RCP 4.5 and 8.5 scenarios. Following data processing and validation of the models, the potential changes in the distribution of WCCEs in the future were investigated based on the projected and historical simulations. WCCEs determined by fixed thresholds were mostly found over high altitudes with a future tendency to reduce particularly under RCP 8.5. On the other hand, WCCEs obtained with percentile thresholds, were distributed mostly in Eastern Greece and Crete while their changes differed significantly among models. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel graph‐based method to classify the regions with similar climate in a local area. We refer our proposed method as graph partition based method (GPBM). Our proposed method attempts to overcome the shortcomings of the current state‐of‐the‐art methods in the literature. It has no limit on the number of variables that can be used and also preserves the nature of climate data. To illustrate the capability of our proposed algorithm, we benchmark its performance with other state‐of‐the‐art climate classification techniques. The climate data are collected from 24 synoptic stations in Fars province in southern Iran. The data include seven climate variables stored as time series from 1951 to 2017. Our results exhibit that our proposed method performs a more realistic climate classification with less computational time. It can save more information during the climate classification process and is therefore efficient in further data analysis. Furthermore, using our method, we can introduce seasonal graphs to better investigate seasonal climate changes. To the best of our knowledge, our proposed method is the first graph‐based climate classification system. [ABSTRACT FROM AUTHOR]

This paper explores the nature and dynamics of adaptation and resilience in the face of a diverse and varied environmental and ecological context using the case study of South Asia's Indus Civilization (ca. 3000-1300 BC). Most early complex societies developed in regions where the climatic parameters faced by ancient subsistence farmers were varied but rain falls primarily in one season. In contrast, the Indus Civilization developed in a specific environmental context that spanned a very distinct environmental threshold, where winter and summer rainfall systems overlap. There is now evidence to show that this region was directly subject to climate change during the period when the Indus Civilization was at its height (ca. 2500-1900 BC). The Indus Civilization, therefore, provides a unique opportunity to understand how an ancient society coped with diverse and varied ecologies and change in the fundamental environmental parameters. This paper integrates research carried out as part of the Land, Water and Settlement project in northwest India between 2007 and 2014. Although coming from only one of the regions occupied by Indus populations, these data necessitate the reconsideration of several prevailing views about the Indus Civilization as a whole and invigorate discussion about human-environment interactions and their relationship to processes of cultural transformation. [ABSTRACT FROM AUTHOR]

Physical-natural, economic, social, and spatial factors constitute processes that, when associated, form and give meaning to the spatial complexity of the Metropolitan Region of Rio de Janeiro, configuring it as a susceptible and vulnerable space to the occurrence of climate exceptionalities. Thus, this paper aims to analyze the spatial and temporal variability of extreme precipitation events in the Metropolitan Region of Rio de Janeiro. Daily data regarding 48 rainfall stations were used: 40 stations in a series from 2008 to 2019 and 8 stations concerning the period between 1970 and 2019. The percentile technique was used to establish standard years (1, 10, 90, and 99 percentile) and to define the extremes (99th percentile). The R software was used to perform autocorrelation tests, to extract the RX1Day and RX5Day indices, and to perform the Mann-Kendall, Pettitt, and Sen's Slope tests. The influence of altitude, slope orientation, and sea level was verified on the spatial distribution of precipitation and extreme events. The years with the highest occurrence of extremes, as well as concentrated and persistent events of greater magnitude, are identified as rainy and extremely rainy, namely 1988 e 2009. 90% of extremes occur in the rainy season, especially during December, January, and March. However, April manifests extremes of a greater magnitude. Four stations with negative and two with positive trends were identified. [ABSTRACT FROM AUTHOR]

Simulations of future climate contain variability arising from a number of sources, including internal stochasticity and external forcings. However, to the best of our abilities climate models and the true observed climate depend on the same underlying physical processes. In this paper, we simultaneously study the outputs of multiple climate simulation models and observed data, andwe seek to leverage theirmean structure aswell as interdependencies thatmay reflect the climate's response to shared forcings. Bayesian modeling provides a fruitful ground for the nuanced combination of multiple climate simulations. We introduce one such approach whereby a Gaussian process is used to represent a mean function common to all simulated and observed climates. Dependent random effects encode possible information containedwithin and between the plurality of climatemodel outputs and observed climate data. We propose an empirical Bayes approach to analyze suchmodels in a computationally efficientway. Thismethodology isamenable to theCMIP6model ensemble, andwe demonstrate its efficacy at forecasting global average near-surface air temperature. Results suggest that thismodel and the extensions it engenders may provide value to climate prediction and uncertainty quantification. [ABSTRACT FROM AUTHOR]

This discussion does not go into specific statistical details. It expresses the impressions of a climatologist when reading a paper about statistical methodologies applied on climate timeseries. Thus the main goal of the discussion is a typical interdisciplinary one. Some of the statistical results of the Battaglia and Protopapas paper are commented versus findings and also questions arising from the underlying physics of the climate system including also some climate impacts. [ABSTRACT FROM AUTHOR]

This study was driven by a need to clarify how variations in climate might affect intense rainfall and the potential for flooding. Sub-daily durations are of particular interest for urban applications. Worldwide, few such observation-based studies exist, which is mainly due to limitations in data. While there are still large discrepancies between precipitation data sets from observations and models, both show that there is a tendency for moist regions to become wetter and for dry regions to become drier. However, changes in extreme conditions may show the opposite sign to those in average conditions. Where changes in observed intense precipitation have been studied, this has typically been for daily durations or longer. The purpose of this two-part study is to examine daily and sub-daily rainfall extremes for evidence of non-stationarity. Here the problem was addressed by supplementing one long record (Part 1) by a set of shorter records for a 30-yr concurrent period (Part 2). Variations in frequency and magnitude of rainfall extremes across durations from 6 min to 72 h were assessed using data from sites in the south-east of Australia. For the analyses presented in this paper, a peaks-over-threshold approach was chosen since it allows investigating changes in frequency as well as magnitude. Non-parametric approaches were used to assess changes in frequency, magnitude, and quantile estimates as well as the statistical significance of changes for one station (Sydney Observatory Hill) for the period 1921 to 2005. Deviations from the long-term average vary with season, duration, and threshold. The effects of climate variations are most readily detected for the highest thresholds. Deviations from the long-term average tend to be larger for frequencies than for magnitudes, and changes in frequency and magnitude may have opposite signs. Investigations presented in this paper show that variations in frequency and magnitude of events at daily durations are a poor indicator of changes at sub-daily durations. Studies like the one presented here should be undertaken for other regions to allow the identification of regions with significant increase/decrease in intense rainfall, whether there are common features with regards to duration and season exhibiting most significant changes (which in turn could lead to establishing a theoretical framework), and assist in validation of projections of rainfall extremes. [ABSTRACT FROM AUTHOR]

Sea surface height (SSH) variability is presented over the Black Sea during 1993–2005. The 1/4° × 1/4° resolution daily SSH fields are formed using optimal interpolation of available altimeter data. SSH variability reveals distinct maxima in the eastern and western basins, reflecting variations in the corresponding gyres. A joint examination of SSH and sea surface temperature (SST) indicates strong relationship between the two only in winter, with correlations as high as 0.6 or more. This would reflect a steric change in sea surface height due to thermal expansion averaged over a relatively deep winter mixed layer. Newly developed SSH fields also demonstrate a switch to the positive mode of SSH starting from the end of 1996 lasting ≈4 yr. Such a climatic shift is found to be strongly related to large-scale teleconnection patterns. Finally, the daily SSH and SST anomaly fields presented in this paper can supplement various applications in the Black Sea, such as examination of biological production and mesoscale eddy dynamics. [ABSTRACT FROM AUTHOR]