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The photosynthetically active radiation (PAR) ratio of 0.45 has been applied in numerous ecological studies to convert global solar radiation (H) to PAR by ordinary multiplication. The ratio is essentially useful and convenient, particularly when in situ measurement of climate data sets are not readily available due to inadequate instrumentation network and experience required. Up till now, there is no general agreement on whether the ratio is 0.45 and how the solar fluxes are distributed in Nigeria or around the world. Numerous empirical ratios have been reported in literature. This study proposed five (5) machine learning models (bagging, boosting, artificial neural network (MLP), automatic regression integrated moving average (ARIMA), controlled ARIMA), and 5 Physics-based models (Physics-based Gumbel model (PGM), popularly applied ratio (PCR), observed PAR ratio (POR), empirical, and PGM-CARIMA hybrid models. The 10 models were used to predict PAR using generalized datasets in Nigeria and the PGM-CARIMA model emerged as the best performing fit based on the R2, RMSE, MAPE, and BIC error metric indicators. PGM-CARIMA reported a better estimate for predicting PAR compared to POR and PCR with a relative error ranging from 0.00002 to 0.0503% compared to POR and PCR which recorded 4.657% and 6.178%. This implies that the use of PGM-CARIMA is more suitable for predicting the PAR ratio as it introduces a lower marginal error of approximately 0.00002–0.0503% when applied for the calculation of net primary productivity (NNP) and of climate change with a significant reduction from 6.17% and 4.65% was achieved in using the ratio of PCR and POR, respectively. Using the proposed PGM-CARIMA evolutionary hybrid model, we demonstrated that the annual mean of the ratio yielded a range of 0.4377 to 0.4712 in Nigeria. The authors also proposed the Physics-based classical climate change model (PCM) to assess the impact of climate change on PAR productivity in Nigeria. The findings revealed that changes in PAR productivity as a result of the impact of climate change may accelerate food production and crop yields in Southern Nigeria and depreciate overall food production and crop yields in Northern Nigeria in 2030, 2040, and 2021–2040. The results of the climate analysis also revealed that north-central Nigeria often referred to as the nation's food basket is most negatively impacted by the impact of climate change externalities over the periods studied. The authors recommended mitigation and adaptation plans such as delayed planting dates, intercropping, routine planting of tropical forages, especially livestock that feed on plants, could be key to climate change mitigation, also, expansion of the planting area as well as the incorporation of improved seedlings that are highly resistant to extreme temperatures will accelerate food security across the country within the periods studied. [ABSTRACT FROM AUTHOR]

Climate change mitigation and renewable resources utilization are becoming particularly urgent for socioeconomic sustainability and human survival. This study develops an integrated model (named as GCMs-RF-FA) for long-term wind energy resources prediction through incorporating multiple global climate models (GCMs), random forest (RF) and factorial analysis (FA) techniques within a general framework. GCMs-RF-FA is capable of (i) dealing with the heterogeneity of structures and parameters in multiple GCMs, (ii) addressing the nonlinear/discrete relationships and uncertainties between the variables, and (iii) identifying the individual and interactive effects of main factors on wind resources prediction. GCMs-RF-FA is then applied to Jing-Jin-Ji region for wind energy prediction under two representative emission scenarios (RCP4.5 & RCP8.5) for 80 years. Multiple validation coefficients prove that the proposed model is effective and feasible. Results reveal that the main factors affecting wind power density (WPD) prediction and their effects at different sites. Results also show that (i) the predicted wind energy value under RCP4.5 is lower than that under RCP8.5; (ii) the seasonal difference of wind energy is obvious, high in winter and spring, low in autumn and summer; (iii) in 2060, the shares of wind energy resources in Beijing, Tianjin and Hebei account for 17.8%, 11.6% and 46.1% of total energy supply, respectively. [ABSTRACT FROM AUTHOR]

Climate change is related to human well-being and the health of the planet. Education is a critical component of global action to mitigate climate change, and young people are future citizens in the fight against climate change. This article discusses the factors that affect adolescents' willingness to continue participating in climate change governance actions. First, based on an expansion of the Theory of Planned Behavior (TPB), the article conducts partial least square-structural equation modeling (PLS-SEM) analysis on the data collected before adolescents participate in climate change education courses and clarifies the factors that significantly impact adolescents' behavioral willingness to participate in climate change governance actions. The results showed that emotions (EMO, β = 0.323) had the most significant effect on the willingness to engage in social good (SG) behaviors, followed by attitudes toward environmental protection (ATEP, β = 0.198) and pro-natural emotion (PNE, β = 0.123). Secondly, the researchers devised an exploratory climate change education curriculum and validated its efficacy through empirical research. The single group comparison of before and after lessons results shows that the exploratory climate change education curriculum can improve the scores of the nine measured factors. Higher effect sizes for attitudes toward environmental protection, confidence in protecting the environment, and pro-natural emotion. This study provides a reference theoretical framework and curriculum model for developing climate change education courses. Effective climate change education courses are crucial in improving youth climate literacy and guiding them to respond scientifically to climate change. [Display omitted] • Expand TPB and evaluate the factors that affect adolescents' willingness to participate in climate change behaviors. • The regional dimension difference analysis is carried out on Chinese adolescents' perception of climate change. • It has provided an effective educational model for advancing climate change education in developing countries. [ABSTRACT FROM AUTHOR]

The ongoing global warming is projected to persist throughout the entire century if effective mitigation measures are not put in place. The warming is associated with occurrence of extreme climate events that are prone over Africa. This study uses model data from 24 CMIP6 modeling centers to investigate future changes in extreme climate events over Africa under 1.5 °C, 2.0 °C and 3.0 °C global warming levels (GWLs). The amplified impact in extreme climate events by additional is computed relative to GWL1.5 °C. The models generally simulate observed climate extremes well, with the ensemble mean minimizing data uncertainties. The entire continent is projected to get warmer with the Sahara, Kalahari and parts of the Mediterranean depicting relatively higher temperatures. Both Rx1day and Rx5day are projected to increase over the continent except for the northern and southern regions of the continent. Rx5day is projected to increase by 4–10% for GWL1.5 and GWL3.0 °C. In comparison, an additional 0.5 °C warming between GWL2.0 °C and GWL1.5 °C will lead to an increase of about 43% impact for the climate extremes. An additional GWL1.5 °C (GWL3.0 °C) could result in about 68% increment in climate extremes. The amplified impact of global warming will result in devastating socioeconomic impacts over Africa. This stresses the need of formulating and implementing more ambitious climate change mitigation measures in effort to limit global warming to 1.5 °C. • Future changes in extreme climate are projected over Sahara, Kalahari and parts of the Mediterranean region. • Higher increase in Rx5day compared to Rx1day is projected for GWL1.5 – GWL3.0 °C. • Additional 0.5 °C (GWL1.5 °C) warming will lead to an increase of about 43% (68%) impact for the climate extremes. [ABSTRACT FROM AUTHOR]

• We assessed the climate change impact on wheat production in China. • We employed the FGLS, DOLS, and D–K estimation techniques. • Temperature negatively affected wheat production in the long–run. • Precipitation contributed positively to wheat production in the long-run. • Agro–chemical input (fertilizer use) enhanced wheat production in the long–run. Northern and Southern regions are the two major wheat production belts of China. Agro–climatic resources have changed significantly in recent years, threatening staple food production in these regions. As a result, it is critical to investigate the long–term effect of climate change and agri–inputs on wheat production in China's top six wheat producing provinces (Hebei, Shandong, Henan, Jiangsu, Anhui, and Hubei) from 1995 to 2020. The estimated long-run results from the FGLS, DOLS, and D–K estimators revealed that climatic factors, specifically temperature, had a negative impact on wheat production, while precipitation had a positive impact. The findings also revealed that agrochemical inputs, such as fertilizer, increases wheat production, whereas pesticide use decreases wheat production significantly. The results from the Dumitrescu–Hurlin panel causality test showed a bidirectional causality between rainfall, fertilizer use, and cultivated area with wheat production, while a unidirectional causality association is running from labor force to wheat production. The current study provides several critical policy recommendations for China's sustainable food production through green agricultural practices and robust climate mitigation strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

Understanding future climate change is crucial for securing and managing water supplies necessary for agricultural activities in China, particularly in rainfed agriculture regions. This paper evaluates the projected climate change for nine major agricultural zones in China, using ensembles of regional climate models (RCMs) simulated under the Coordinated Regional Downscaling Experiment-East Asia second phase (CORDEX-EA-II) framework. The simulations were conducted for two climate scenarios (RCP2.6 and RCP8.5) at a 25 km grid spacing, with three RCMs (RegCM4, REMO, and WRF) driven by multiple CMIP5 global climate models (GCMs). The study compares the RCM simulations to the driving CMIP5 and the recent CMIP6 ensembles, focusing on surface temperature, precipitation, and surface relative humidity for the present day (1981–2010), mid-future (2036–2065), and far future (2070–2099) periods. The model evaluations indicate that the GCM and RCM simulations are consistent with observations, with RCMs showing better performance than the driving GCMs. The results show that both GCMs and RCMs project increased temperature and precipitation in most agricultural zones of China in the future, with the CMIP6 ensembles projecting the largest temperature increase and the CORDEX-EA-II ensemble showing the finest spatial details. Relative humidity is projected to decrease over major rice-producing areas of South China in the CORDEX-EA-II, CMIP5, and CMIP6 ensembles. This study provides valuable information to support climate change adaptation and mitigation efforts in China, particularly in the agricultural sectors. • CORDEX-EA-II simulations demonstrate better model performance over China. • Temperature and precipitation are projected to increase in the future. • Relative humidity is projected to decrease over South China. [ABSTRACT FROM AUTHOR]

Climate models project higher temperatures and a substantial net water deficit during the summer months over Germany until the end of the 21st century. The agricultural sector is particularly vulnerable to changing weather and climate conditions. In this study, we examine the role of irrigation for potato yields as a climate change adaptation measure in Northeast Lower Saxony (Germany). The region represents the largest irrigated area in Germany and is one of the main growing areas of potatoes. We develop a statistical multivariate regression model to investigate whether regional climate and irrigation data at the monthly and county level are suitable to describe potato yield variability from 1978 to 2018. Irrigation is estimated based on the climatic water balance and crop water demand. These estimates are calibrated against irrigation data from local agencies to account for realistic monthly water withdrawals. We analyze future yield changes based on climate model projections for the 21st century and different irrigation scenarios. We find that the potato yield will decrease significantly by 18% on average until 2050 with climate projection uncertainty of ±6.4% under the high emission scenario RCP 8.5 and without irrigation. Irrigation at current levels could compensate for about 54% of these yield losses. Doubling the amount of irrigation minimizes yield losses by as much as 80%. Under the low (middle) emission scenario RCP 2.6 (RCP 4.5), potato yield are projected to decrease moderately by 4.5% ± 3.4% (5.4% ± 2.8%) with irrigation at current levels. We show that the yield losses mainly originate from higher temperatures during the summer. Our analysis indicates that the capacity of irrigation to minimize yield losses is limited. Strengthening climate change mitigation efforts may be more effective in preventing yield losses. However, the effect of elevated CO 2 might buffer the high yield losses, especially in RCP 8.5. The studies supports farmers and decision makers in assessing future risks in potato yields. It can guide policymakers in allocating economic resources to cope with climate change. [Display omitted] • Potato yields in Germany will decrease sharply under climate change for RCP8.5. • Irrigation at current levels mitigates about half of the yield losses. • Irrigation potential is limited because it can not buffer high temperatures. • Climate change mitigation and other adaption actions need to complement irrigation. • The effect of elevated CO2 might buffer high yield losses, especially in RCP8.5. [ABSTRACT FROM AUTHOR]

[Display omitted] • The Budyko framework was applied to 75 catchments covering a large area of Taiwan. • Varied landscape effects determined water resources availability among catchments. • Considerable spatial variability in the Budyko-related variables were found. • Three clusters were identified based on catchments' movements in the Budyko space. • 64 catchments will face the reduced availability of future water resources. Assessing water resources availability in a changing climate is a determinant of promoting sustainable development in a region. Such assessment should better be inter-basin in order to unveil a region's overall risk, so a time-varying Budyko framework is developed and adopted in this study. Gauge-based gridded precipitation and temperature, in company with runoff data, were used to derive the Budyko curves for 75 catchments over Taiwan. Downscaled precipitation and temperature data from selected global climate models and Representative Concentration Pathways (RCPs) were then used to calculate the shifts in the Budyko space of each catchment. It was found that the Budyko-related variables (e.g., evaporative and aridity indices, and catchment-specific parameter) exhibited considerable variability island-wide, with a few outliers in certain catchments being modulated by distinct landscape features and/or human activities. Under all RCPs analyzed, the majority of catchments showed the magnified movement vectors pointing at the 1st quadrant in the Budyko space in the late-21st century, suggesting a predominant and aggravating drying trend over Taiwan. Cluster analysis of the movement vectors under RCP8.5, which entailed the least inter-model differences, identified three major clusters, two of which (64 catchments included) indicated a drying trend of varied significancy. According to our findings, without effective mitigation of climate change, an increased likelihood of the worst-case scenario will substantialize the reduced availability of Taiwan's water resources. [ABSTRACT FROM AUTHOR]

• Impacts of climate change on the energy demand and thermal comfort of buildings. • 13 future climate scenarios with 90 years of hourly data in 38 European cities. • Long- and short-term variations considering climate extremes and uncertainties. • Extreme climate events notably affect peak loads and thermal comfort of buildings. • Cooling demand can increase to 28% on average while heating decrease for 16%. In recent years, climate change and the corresponding expected extreme weather conditions have been widely recognized as potential problems. The building industry is taking various actions to achieve sustainable development, implement energy conservation strategies, and provide climate change mitigation. In addition to mitigation, it is crucial to adapt to climate change, and to investigate the possible risks and limitations of mitigation strategies. Although the importance of climate change adaptation is well-understood, there are still challenges in understanding and modeling the impacts of climate change, and the consequent risks and extremes. This work provides a comprehensive study of the impacts of climate change on the energy performances and thermal comfort of European residential building stocks. To perform an unbiased assessment and account for climate uncertainties and extreme events, a large set of future climate data was used for a 90-year period (2010–2099). Climate data for 38 European cities in five different climate zones, downscaled by the "RCA4" regional climate model, were synthesized and applied to simulate the respective energy performances of the residential building stocks in the cities. The results suggest that there will be larger needs for cooling buildings in the future and less heating demand; however, there are differences in the variation rates between zones and cities. Discomfort hours will increase notably in cities within cooling-dominated zones, but will not be affected considerably in cities within heating-dominated zones. In addition to long-term changes, climate-induced extremes can considerably affect future energy demands, especially the cooling demand; this may become challenging for both buildings and energy systems. [ABSTRACT FROM AUTHOR]

As climate change is likely to alter regional water availability via shifted precipitation patterns that affect agricultural practices, the analysis of the effectiveness of bioenergy promotion policies and efficiency of bioenergy development requires a thorough consideration of the interrelationships among water, agriculture, and renewable energy development in the context of climate change. We propose a stochastic, price-endogenous mathematical programming with recourse model by linking a well-established water resource model to a bioenergy production framework to investigate the aggregate economic and environmental effect of bioenergy production. This study theoretically analyzes the optimal resource allocation and production conditions of the model, and empirically tests the goodness of the proposed model with observed data. The results show that the proposed framework is likely to reflect the actual bioenergy production in the face of climate-induced impacts, and at small to moderate climate impacts the bioenergy production is likely to be more influenced by the energy and emission prices than by climate-induced yield changes. In addition, while the reduction in precipitation would not result in significant effects on bioenergy production, a substantial change in land use and cropping decisions might occur. • A bioenergy production–water allocation model under climate change is presented. • Optimal resource allocation and production conditions were analyzed and tested. • Uncertain supply of water can substantially influence bioenergy production. • Water supply–agriculture relationship must be aggregated for bioenergy potential. [ABSTRACT FROM AUTHOR]

This paper explores the epistemological basis for economic prognostications of climate change activists arising from, arguably, the most important scientific problem confronting modern society: the facts, beliefs and theories associated with the measurement, causes, impact and mitigation of climate change. This facilitates identification of the role that rhetoric plays in activist claims associated with the economics of global warming and climate change. Following a rudimentary discussion of ancient views on rhetoric, the connection of rhetoric with anti-foundational epistemology and language is identified. Criteria for determining when language is rhetorical are specified. Exploiting this philosophical background, mitigation proposals of climate change activists derived from models of climate change economics are critiqued. • Clarifies the role of rhetoric in climate change mitigation proposals. • Provides background on the historical development of rhetoric that is absent for available literature in ecological economics. • Critiques prognostications of economic models for climate change mitigation proposals. • Explores the connection between rhetoric, epistemology and language in the context of climate change activism. [ABSTRACT FROM AUTHOR]

The impacts of climate change and extreme weather events (e.g. frost-, heat-, drought-, and heavy rainfall events) on the continuous phenological development over the entire seasonal cycle remained poorly understood. Previous studies mainly focused on modeling key phenological transition dates (e.g. discrete timing of spring bud-break and fall senescence) based on aggregated climate variables (e.g. mean temperature, growing-degree days). We developed and evaluated a Bayesian Hierarchical Space-Time model for Land Surface Phenology (BHST-LSP) to synthesize remotely sensed vegetation greenness with climate covariates at a daily temporal scale from 1981 to 2014 across the entire conterminous United States. The BHST-LSP model incorporated both temporal and spatial information and exhibited high predictive power in simulating daily phenological development with an overall out-of-sample R2 of 0.80 ± 0.17 and 0.72 ± 0.20 for spring and fall phenology, respectively. The overall out-of-sample normalized root mean square errors were 9.3% ± 6.1% and 9.9% ± 5.2% between the observed and predicted vegetation greenness for spring and fall phenology, respectively. We found that a fast increase of temperature can accelerate the speed of spring green-up while a slow decrease of temperature can lead to a decelerated fall brown-down. Increasing accumulated precipitation can benefit daily phenological development over an entire growing season, while extreme rainfall events can have the opposite effects. More frequent frost events could slow spring leaf expansion and accelerate fall leaf senescence. Impacts of extreme heat events were complex and depended on water availability. Cropland in the Midwest as well as evergreen needleleaf forest along the coastal regions showed relatively strong resistance to drought events compared to other land cover types. The BHST-LSP model can be used to forecast vegetation phenology given future climate projection, thus providing valuable information for adopting climate change adaptation and mitigation measures. • A Bayesian model was developed to simulate continuous phenological development. • Rate of daily temperature changes impact speed of both spring and fall phenology. • Increased precipitation benefit phenological development over an entire season. • Frost events slow spring leaf expansion and accelerate fall leaf senescence. • Cropland and evergreen forest show resistance to drought events. [ABSTRACT FROM AUTHOR]

• Available Water Capacity does not represent the capacity of soils to supply water to plants. • Real Soil Moisture Supply Capacities can be obtained by dynamic modeling. • Increasing Soil Organic matter does not significantly increase MSC in clayey soils. • When modeling the impact of climate change, soil properties have a major impact. The Available Soil Water Capacity (AWC) is standard data in most soil databases and expresses soil water contents in the rootzone between field capacity (FC; –33 kPa) and permanent wilting point (WP; −1500 kPa). Literature suggests that increasing the content of soil organic matter (SOM) of a given soil does not significantly increase AWC and this has important implications when estimating soil moisture supply to crops and evaluating the potential for climate mitigation. For most crops, the real FC values vary between −10 and −50 kPa in different soils and WP values between −800 and −1500 kPa. Thus standard values for AWC of FC and WP do not represent field conditions in many soils. When exploring AWC for six Italian soil series, ranging from clay to sandy, AWC values at increasing %SOM were lower in clay soils and higher in sand as compared with actual conditions, which could be explained by considering the shape of the corresponding moisture retention curves. Rather than focus on static AWC values to define moisture supply to plants, real or actual soil moisture supply capacities (MSC) can be obtained by dynamic modeling of the soil-water-atmosphere-plant system, including a "sink-term" indicating a continuous relation between water uptake and negative pressure head of soil water and evaporative demand. Also, only models allow exploration of the effects of future severe IPCC climate scenario RCP 8.5. Thus, studying MSC for the six Italian soil series showed that MSC values were: (i) on average 30% higher than the corresponding AWC; (ii) distinctly different for the six soils; (iii) affected by declines of 1–9% as a result of the effects of future climate scenarios.; (iv) not significantly affected by increases of %SOM when considering climate change, except for the sand. Generalizations as to the effect of future climate scenarios and %SOM on MSC can only be realistic when modeling is performed for soil series in different climate zones. [ABSTRACT FROM AUTHOR]

• A climate change impact model is proposed for forest management. • The vulnerability and resilience degree of forest to climate change was analysed. • Ecological and management indicators have been developed for forest systems. Global warming has caused significant environmental and socio-economic impacts on the global and local scales. Based on the definitions of vulnerability and resilience provided by the Intergovernmental Panel on Climate Change, some ecological and management indicators have been developed for forest systems. Using Dempster-Shafer's theory of evidence, forests were subdivided into different classes of vulnerability and resilience. Results enabled the estimation of economic damages to forests by 2050. The findings of this study are useful in promoting further research and guiding management decisions towards sustainable environmental policies on the monitoring and mitigation of climate change damage. [ABSTRACT FROM AUTHOR]