Effects Of Climate Change Research Articles

Nonlinear effects of climate change on tourism development: evidence from China

Purpose The purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions. Design/methodology/approach Based on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism. Findings The study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution. Originality/value First, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al., 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.

The Impact of Weather Shocks on Violent and Property Crimes in Jamaica

Abstract Developing countries face the largest exposure to the negative effects of climate change. However, as temperature and rainfall patterns change, we have a limited understanding of their impact on these countries and the mitigation strategies that may be needed. In this paper, we utilize administrative panel data to examine the impact of weather shocks on violent and property crimes in Jamaica. We find strong evidence that a one-standard-deviation increase in the daily temperature (2○C) increases violent crime by 3.67 percent, due to an increase in the number of murders (3.44 percent), shootings (7.53 percent), and cases of aggravated assault (6 percent). However, our results suggest that temperature changes have no statistical impact on property crime. In addition, we find that a one-standard-deviation increase in rainfall (2 mm) reduces crimes such as shootings (1.53 percent), break-ins (2.27 percent), and larcenies (3.85 percent), but it has a minimal impact on other categories of crime.

Sustainable rural livelihoods in the face of climate-induced hazards in Pakistan: Nexus of coping mechanisms, determinants, and benefits

This study comprehensively examined the climate change susceptibility hazards and the subsequent adaptation strategies adopted by the farming community in Punjab, Pakistan, to mitigate the adverse impacts on rural livelihoods. Data were collected through a household survey involving 1080 respondents, using a multistage random sampling approach. Binary Logistic Regression was employed to discern the determinants driving the adaptation strategies. Additionally, the benefits derived from these adaptation measures were quantified, using Partial Least Squares Structural Equation Modelling (PLS-SEM). The impact of climate change on rural livelihoods was found to be significant, leading to substantial degeneration. Farmers' income from crops, livestock products, machinery, tube wells, orchards, and vegetables have been adversely affected. The adaptation strategies employed to mitigate these adverse effects encompassed crop diversification, divergence of livestock populations, seeking non-farm income sources, adjusting herd composition, utilizing governmental subsidies, adopting new animal breeds and crop varieties, selling livestock animals for sustenance, accessing loans from governments/banks, sharing labour during years of climate change, communal forage sharing, and insuring crops and livestock. These adaptation strategies are intricately influenced by socioeconomic, demographic, agronomic, and climate-related information, and forecasting parameters as well. Notably, adapters experienced fewer impacts of climate change compared to non-adapters. To bolster resilience, it is imperative to introduce climate-resilient crop varieties and animal breeds. Additionally, enhancing financial access for farmers is crucial, necessitating targeted strategies devised by the government to mitigate the adverse effects of climate change. This study emphasized the urgency for targeted and comprehensive strategies to navigate rural communities towards sustainable livelihoods in an evolving climate landscape.

Evaluating plans for sustainable developmentin Arctic cities.

Cities located in the Arctic often have extreme geographic and environmental contexts and unique sociopolitical and economic trajectories that, when combined with amplified effects of climate change in the region, impact future sustainable development. Well-recognized and standardized sustainable development indicator (SDI) frameworks such as ISO 37120 or UN-Habitat City Prosperity Index are often used to compare data across cities globally using comprehensive sets of indicators. While such indexes help characterize progress toward development and guide short- and long-term decision-making, they often lack relevance to specific contexts or characterize future visions of urban growth. To evaluate the extent of these deficiencies and to provide a comparative analysis of approaches to sustainable urban growth in the Arctic, this paper analyzes city planning documents for five northern cities - Anchorage (USA), Utqiagvik (USA), Reyjavik (ISL), Iqaluit, (CAN), Whitehorse, (CAN) - for goals, targets, and indicators and compare these to thematic areas and indicators defined by ISO 37120:2018 Sustainable Cities and Communities. The results confirm that although international SDI frameworks may be useful for comparative analysis of cities across diverse regions, they exclude important local factors that influence goal-oriented urban sustainability planning strategies employed in the Arctic region.

A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness.

Determination of polymorphisms in the HSP90AA1 gene region in some Turkish sheep populations by AS-PCR

Heat shock proteins (HSPs) are molecular chaperones protecting living cells from the negative effects of extreme ambient temperatures. In this study, genetic polymorphisms of the HSP90AA1 gene were monitored via Allele-Specific Polymerase Chain Reaction (AS-PCR) in three native Turkish sheep populations namely İvesi (IVS), Güney Karaman (GKR), and Karakaş (KRK). The HSP90AA1 was polymorphic in all populations yielding two alleles (C and G) and three genotypes (CC, CG, and GG). The G allele frequency was higher than the C allele frequency in all populations. The lowest (0.100) and highest (0.246) CC frequency was observed in KRK and IVS, respectively, while the GG genotype frequency varied between 0.250 (GKR) and 0.600 (KRK). Significant deviation (p<0.001) from Hardy-Weinberg Equilibrium (HWE) was detected in the IVS breed in terms of the HSP90AA1 gene. The genetic distance-based phylogenetic tree indicated that GKR was genetically different from IVS and KRK populations in terms of the HSP90AA1 polymorphism. These variations regarding the HSP90AA1 gene should be conserved, since the negative effects of global warming and climate change are expected to be more hazardous in the future. Besides, these genetic variations may be utilized by the farmers to design comprehensive selection strategies against heat stress in native Turkish sheep populations.

Temporal change in the forest birds of northeastern Connecticut shows partial concordance with predicted effects of climate and habitat change

Temporal change in the forest birds of northeastern Connecticut shows partial concordance with predicted effects of climate and habitat change

UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models

Abstract Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias correction approaches usually focus on correcting lower-order statistics (mean, standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.

A toxicological perspective on climate change and the exposome.

Climate change is accompanied by changes in the exposome, including increased heat, ground-level ozone, and other air pollutants, infectious agents, pollens, and psychosocial stress. These exposures alter the internal component of the exposome and account for some of the health effects of climate change. The adverse outcome pathways describe biological events leading to an unfavorable health outcome. In this perspective study, I propose to use this toxicological framework to better describe the biological steps linking a stressor associated with climate change to an adverse outcome. Such a framework also allows for better identification of possible interactions between stressors related to climate change and others, such as chemical pollution. More generally, I call for the incorporation of climate change as part of the exposome and for improved identification of the biological pathways involved in its health effects.

İklim değişiminin Türkiye’de güvenli gıda algısı ve tüketim davranışlarına etkisinin incelenmesi

İklim değişikliği ile gıda üretim zincirinde meydana gelen zaruri değişimler tüketicinin tutum ve davranışlarını etkilemekte ve piyasa düzenini şekillendirmektedir. Yapılan bu çalışmada, üretimde yaşanan değişikliklere tüketicinin bakış açısı sorgulanmış, tutum ve davranışları incelenmiştir. Araştırma, ülke genelini temsilen 40 ilde 1013 kişiye anket uygulaması ile gerçekleştirilmiştir. Bulgulara göre, tüketicilerin %90’ı yerli ürünleri taze haliyle tercih etmekte, taze ürüne ulaşılamadığında ise dondurulmuş (%36.75) ürünleri satın almaktadır. Gıda tüketiminde en çok endişe duyulan unsur, genetiği değiştirilmiş organizmalardan (GDO) elde edilen ürünler (%40.32) olarak tespit edilmiştir. Gıda kaynaklı hastalanmalara mikroorganizmaların (%50.30) neden olduğu ifade edilmiştir. Ayrıca gıdanın güvenli olmasının önemsendiği (%26.06), ve gıdanın güvenli olması halinde lezzet (%4.87), görüntü (%15.63), ucuzluk (%42.31) gibi kriterlerin dikkate alınmadığı belirtilmiştir. Sonuç olarak, Türkiye’de tüketicilerin bilimsel veriler ile aydınlatılması, endişe duyulan birçok konunun iklim değişimi ile daha da iç içe olacağının ifade edilerek, farkındalıklarının artırılması gerektiği tespit edilmiştir.

Development and Standardization of a Climate Vulnerability Index to Measure the Vulnerability Indices of Paddy Growers

India is prominently recognized as the foremost producer of paddy, cultivating this crop over 47.83 million hectares and generating 135.75 million tonnes of paddy, thus playing a substantial role in the worldwide paddy output. However, there is an anticipated decline in paddy production yields due to the projected effects of climate change, estimated to range from 10% to 30% by 2030. The assessment of vulnerability will give a comprehensive picture of current and future climate change risks with more stress factors to be anticipated. It will help identify opportunities arising from climate change, and provide information on how to assess adaptive capacity and cope with uncertainty. Adaptation cannot be planned based on the climate projections; information on risk and vulnerabilities is also needed to determine how the climate interacts with socio-economic issues. Against this backdrop, the proposed research seeks to fill this crucial knowledge gap by developing and constructing vulnerability indices tailored specifically for paddy growers in India. Based on review of literature and discussion with experts, three dimensions along with indicators and sub-indicators by adopting the indicator approach method under the vulnerability indices of paddy growers due to climate change were identified. The relevancy rating score was obtained from 50 experts in the concerned area. Based on the relevancy score, 17 indicators and 65 sub-indicators of 0.80 and above were considered for inclusion in the vulnerability index. To compute the index values for each of the identified dimensions, their relative importance in the vulnerability was worked out by assignment of weights to indicators and sub-indicators under each dimension through Principal component analysis (PCA) and the findings revealed that sensitivity was observed to be in the top position (8.36), followed by exposure (8.28) and adaptive capacity (5.61) in assessing the climate vulnerability of paddy growers.

Interactive effects of soil moisture, air temperature and litter nutrient diversity on soil microbial communities and Folsomia candida population

Soil organisms play a key role in carbon and nutrient cycling in forest ecosystems. While soil organisms are strongly influenced by litter chemistry and are highly sensitive to abiotic conditions, little is known about the interactive effects of these two factors. To address this gap in knowledge, we conducted a 10 week microcosm experiment in which we simulated the effects of climate change on soil ecology. More specifically, we studied relationships among litter nutrient concentration, microbial biomass, Collembola demographic parameters, and litter decomposition, exploring the potential impacts of increasing air temperature and decreasing soil moisture. To develop a gradient of nutrient concentrations, we created six tree litter mixtures with materials gathered from Quercus pubescens and its companion species. In contrast to microbes, we observed that Collembola abundance and litter decomposition were interactively affected by soil moisture and air temperature: the negative effect of increasing air temperature on Collembola abundance was amplified by reduced soil moisture, whereas the positive effect of increasing air temperature on litter decomposition disappeared under reduced soil moisture conditions. In contrast to fungi, the response of bacterial biomass and Collembola abundance to litter nutrient concentration was dependent on abiotic conditions. More specifically, the relationships between nutrients, especially calcium and magnesium, and bacterial biomass and Collembola abundance were less robust or disappeared under drier or warmer conditions. In conclusion, our findings underscore that ongoing climate change could affect soil organisms directly as well as indirectly, by altering their responses to litter nutrient concentrations. In addition, we found that nutrient‐rich habitats might be more affected than nutrient‐poor habitats by altered climatic conditions.

Development and Standardization of an Adaptation Index to Assess the Climate Resilient Practices by Paddy Growers

India is prominently recognized as the foremost producer of paddy, cultivating this crop over 47.83 million hectares and generating 135.75 million tonnes of paddy, thus playing a substantial role in the worldwide paddy output. However, there is an anticipated decline in paddy production yields due to the projected effects of climate change, estimated to range from 10% to 30% by 2030. In recent times, adaptation to climate change has become a major concern to farmers, policy makers and researchers. Climate resilient rice production practices need to be enhanced at the farm level in order to aid rural residents in improving their household food security. Against this backdrop, the proposed research seeks to fill this crucial knowledge gap by developing and constructing adaptation index tailored specifically for paddy growers in India. Through a literature review and discussions with experts, we have identified indicators and sub-indicators using the indicator approach method. These indicators will help us understand how paddy growers are adapting to climate change and implementing climate resilient practices. The relevancy rating score was obtained from 30 experts in the concerned area. Based on the relevancy score, 8 indicators and 23 sub-indicators of 0.80 and above were considered for inclusion in the adaptation index. To compute the index values for each of the identified indicators, their relative importance in the adaptation practices was worked out by assignment of index values to indicators through Principal component analysis (PCA) based on the high factor loadings exceeded 0.5 of sub-indicators were considered and the findings revealed that disease and management had highest index value of 3.461, followed by methods of paddy establishment (2.195), crop rejuvenation techniques (2.10), altered planting dates (2.049), water saving and management techniques (1.987), nursery management (1.562), paddy varieties (1.342) and spacing (1.214).

Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia

The severity and frequency of extremes are changing; thus, it is becoming necessary to evaluate the impacts of land cover changes and urbanisation along with climate change. A framework of the Generalised Extreme Value (GEV) method, Google Earth Engine (GEE), and land cover patterns’ classification including Random Forest (RF) and Support Vector Machine (SVM) can be useful for streamflow impact analysis. For this study, we developed a unique framework consisting of a hydrological model in line with the Process-informed Nonstationary Extreme Value Analysis (ProNEVA) GEV model and an ensemble of General Circulation Models (GCMs), mapping land cover patterns using classification methods within the GEE platform. We applied these methods in Southeast Queensland (SEQ) to analyse the maximum instantaneous floods in non-stationary catchment conditions, considering the physical system in terms of cause and effect. Independent variables (DEM, population, slope, roads, and distance from roads) and an integrated RF, SVM methodology were utilised as spatial maps to predict their influences on land cover changes for the near and far future. The results indicated that physical factors significantly influence the layout of landscapes. First, the values of projected evapotranspiration and rainfall were extracted from the multi-model ensemble to investigate the eight GCMs under two climate change scenarios (RCP4.5 and RCP8.5). The AWBM hydrological model was calibrated with daily streamflow and applied to generate historical runoff for 1990–2010. Runoff was projected under two scenarios for eight GCMs and by incorporating the percentage of each land cover into the hydrological model for two horizons (2020–2065 and 2066–2085). Following that, the ProNEVA model was used to calculate the frequency and magnitude of runoff extremes across the parameter space. The maximum peak flood differences under the RCP4.5 and RCP8.5 scenarios were 16.90% and 15.18%, respectively. The outcomes of this study suggested that neglecting the non-stationary assumption in flood frequency can lead to underestimating the amounts that can lead to more risks for the related hydraulic structures. This framework is adaptable to various geographical regions to estimate extreme conditions, offering valuable insights for infrastructure design, planning, risk assessment, and the sustainable management of future water resources in the context of long-term water management plans.

Ethanol-enhanced wash-coating of zeolite 13X on microchannel walls for CO2 capture

Minimizing the energy demand for capturing carbon dioxide (CO2) from air and post-combustion sources is critical to mitigate the effects of climate change. Adsorbent-coated microchannels have been shown to significantly enhance the performance of CO2 capture systems. A novel single-dip wash-coating approach is developed in this work using silica nanoparticles and various base solvents to create a uniform and porous zeolite 13X adsorbent layer on cordierite microchannels. Extensive exploration of base solvents is undertaken to enhance adsorption performance and structural integrity, culminating in identifying a 70 % ethanol mixture as the most effective solvent. The coatings are characterized through a materials characterization suite and image analysis in conjunction with CO2 adsorption performance to determine the optimum adsorbent loading conditions. Equilibrium adsorption performance tests are performed for the coated samples in CO2 capture systems. The specific surface area (SSA) of the powdered 13X, initially measured at 662 m2/g, decreased to 553 m2/g in the coated form (utilizing a 20 % solid fraction with 70 % ethanol as the solvent), which is consistent with the observed adsorption performance results. The results underscore the scalability potential of these coated microchannels, presenting a promising alternative for CO2 separation systems to the conventional packed adsorbent beds.

Progress on the Impact of Climate Change on Cardiac Arrest

Climate change is a global public health problem. With the improvement of the industrialization level of various countries, the problem is becoming more and more serious. The mortality and disability rates of cardiac arrest are very high. Climate change affects the occurrence of cardiac arrest, and extreme weather such as high temperature are the risk factors for cardiac arrest. Previous studies have explored the effects of climate change and cardiac arrest, however the study design and exposure assessment of these studies differ, and the outcome associations are inconsistent. This paper aims to review the relationship between climate change and the onset of cardiac arrest and provide a theoretical basis for the prevention and treatment of cardiac arrest.

Knowledge and Reported Practices of Nursing Students Regarding Health Effects of Climate Change in Sohag City

Knowledge and Reported Practices of Nursing Students Regarding Health Effects of Climate Change in Sohag City

How Climate Change Affects Labor Mobility: Empirical Evidence from the CLDS

ABSTRACT Regional climate conditions have become a key factor that is exacerbating the process of China’s labor spatial mobility. In contrast to previous research based on mixed cross-sectional data and floating population sample selection, this study uses unbalanced panel data from the China Labor Force Dynamic Survey (CLDS) from 2014 to 2018 and a regional temperature dataset to examine the impact of climate change on inter-provincial and municipal labor mobility outflow from the perspective of local household registration, and further investigates regional reallocation and stock effects of labor related to climate change. The findings reveal four relevant results. First, after eliminating the influence bias of individual fixed effects, the labor force outflow effect of climate change across regions is significantly smaller than that found in previous research, at only one-fifth of existing research conclusions. Second, the mechanism analysis shows that temperature can affect labor mobility through individual health, happiness and government environmental regulation. Third, the impact of climate change on labor mobility has significant heterogeneity. Labor flows from underdeveloped regions to developed areas and from the central and western regions to the eastern region, and climate change has a greater impact for workers with higher education levels. In addition, climate change has a greater impact on workers with higher family incomes and family age structures. Fourth, the impact of climate change on labor mobility exhibits temperature-based threshold effects. This study provides a new perspective for understanding the impact of climate change on labor mobility, offering valuable policy support for guiding the rational flow of labor and urban managers’ formulation of strategically targeted measures.

Optimal Rotation Age in Fast Growing Plantations: A Dynamical Optimization Problem.

Forest plantations are economically and environmentally relevant, as they play a key role in timber production and carbon capture. It is expected that the future climate change scenario affects forest growth and modify the rotation age for timber production. However, mathematical models on the effect of climate change on the rotation age for timber production remain still limited. We aim to determine the optimal rotation age that maximizes the net economic benefit of timber volume in a negative scenario from the climatic point of view. For this purpose, a bioeconomic optimal control problem was formulated from a system of Ordinary Differential Equations (ODEs) governed by the state variables live biomass volume, intrinsic growth rate, and area affected by fire. Then, four control variables were associated to the system, representing forest management activities, which are felling, thinning, reforestation, and fire prevention. The existence of optimal control solutions was demonstrated, and the solutions of the optimal control problem were also characterized using Pontryagin's Maximum Principle. The solutions of the model were approximated numerically by the Forward-Backward Sweep method. To validate the model, two scenarios were considered: a realistic scenario that represents current forestry activities for the exotic species Pinus radiata D. Don, and a pessimistic scenario, which considers environmental conditions conducive to a higher occurrence of forest fires. The optimal solution that maximizes the net benefit of timber volume consists of a strategy that considers all four control variables simultaneously. For felling and thinning, regardless of the scenario considered, the optimal strategy is to spend on both activities depending on the amount of biomass in the field. Similarly, for reforestation, the optimal strategy is to spend as the forest is harvested. In the case of fire prevention, in the realistic scenario, the optimal strategy consists of reducing the expenses in fire prevention because the incidence of fires is lower, whereas in the pessimistic scenario, the opposite is true. It is concluded that the optimal rotation age that maximizes the net economic benefit of timber volume in P. radiata plantations is 24 and 19 years for the realistic and pessimistic scenarios, respectively. This corroborates that the presence of fires influences the determination of the optimal rotation age, and as a consequence, the net economic benefit.

Interactive effects of chronic ocean acidification and warming on the growth, survival, and physiological responses of adults of the temperate sea urchin Strongylocentrotusintermedius

To investigate the interactive effects of chronic ocean acidification and warming (OAW) on the growth, survival, and physiological responses of sea urchins, adults of the temperate sea urchin Strongylocentrotus intermedius were incubated separately/jointly in acidic (ΔpHNBS = −0.5 units) and thermal (ΔT = +3.0 °C) seawater for 120 days under lab-controlled conditions based on the projected ocean pH and temperature for 2100 put forward by the Intergovernmental Panel on Climate Change (IPCC). Survival rate (SR), average food consumption rate (FCR), gut index (GuI), specific growth rate (SGR), digestive capability, energy production, and antioxidant capability were subsequently determined. The results showed that 1) the SR, FCR, GuI and SGR decreased sharply under OAW conditions. Significant interactive effects of OAW on SR and SGR were observed at 120 days post-incubation (dpi), and on FCR this occurred at 90 dpi. 2) OAW altered the activities of both digestive and antioxidant enzymes. There were significant interaction effects of OAW on the activities of amylase, trehalase, and superoxide dismutase. 3) The relative gene expression levels and activities of key enzymes involved in glycometabolism pathways (i.e., glycolysis and the tricarboxylic acid cycle) were significantly affected by OAW, resulting in an alteration of the total ATP content in the sea urchins. Interaction effects of OAW were observed in both relative gene expression and the activity of enzymes involved in glycolysis (hexokinase), the transformation of glycolysis end-products (lactate dehydrogenase), the tricarboxylic acid cycle (citrate synthetase), and ATP production (Na+/K+-ATPase). The data from this study will enrich our knowledge concerning the combined effects of global climate change on the survival, growth, and physiological responses of echinoderms.