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In the 21st century, the importance of energy generation and carbon emissions in developing countries is indisputable. In the whole wide world, the building stock is responsible for the two fifths of the total world annual energy consumption. Considering the predictions regarding future climate due to climate change, a good understanding on the energy use due to future climate is required. The aim of this study was to evaluate the impact of future weather in the heating demand and carbon emissions for a group of buildings at district level, focusing on an area of London in the United Kingdom. The methodological approach involved the use of geospatial data for the case study area, processed with Python and Anaconda through Jupyter notebook, generation of an archetype dataset with energy performance data and TABULA typology and the use of python embedded in QGIS to calculate the heating demand in the reference weather data, 2050 and 2100 in accordance to RCP4.5 and RCP 8.5 scenarios. A validated model was used for the district level heating demand calculation. On the one hand, the results suggest that a mitigation of carbon emissions under the RCP4.5 scenario will generate a small decrease on the heating demand at district level, so slightly similar levels of heating generation must continue to be provided using sustainable alternatives. On the other hand, following the RCP 8.5 scenario of carbon emission carrying on business as usual will create a significant reduction of heating demand due to the rise on temperature but with the consequent overheating in summer, which will shift the energy generation problem. The results suggest that adaptation of the energy generation must start shifting to cope with higher temperatures and a different requirement of delivered energy from heating to cooling due to the effect of climate change.

The service life of a building or structure is often presumed to be 60 years. Products usedin the building envelope are often covered by a facade material on the exterior side. Hence, the failure of these products is not easily observable, and the repair or replacement is normally not technically nor economically feasible. Thus, these products are expected to endure the entire estimated lifetime of the building. Service life prediction of these products is based on accelerated ageing tests, whose aim is to measure future possibilities of materials‘ durability under their expected service life. Preliminary calculations of acceleration factors are discussed and related to historical and future climate scenarios for a dataset from Calgary, Canada. The changes in temperature threshold values for this dataset is significant. Relating the measured values to a duration of a typical accelerated durability test indicates that the test duration is sufficient for a service life of 102 years according to historical climate, but only 52 years taking into account an assumed climate change.

In the context of global warming, agricultural production and social and economic development are significantly affected by drought. The future change of climate conditions is uncertain; thus, it is of great importance to clarify the aspects of drought in order to define local and regional drought adaptation strategies. In this study, the meteorological data from 1976 to 2005 was used as a historical reference, and nine Global Climate Models (GCMs), downscaling to meteorological stations from 2039 to 2089, were used as future climate data. Based on Penman–Monteith, the reference crop Evapotranspiration (ET0) and Standardized Precipitation Evapotranspiration Index (SPEI) of the reference crop in three emission scenarios of RCP2.6, RCP4.5, and RCP8.5, under future climate conditions, were calculated. A non-parameter Mann–Kendall trend test was performed on temperature, precipitation, ET0, and SPEI to analyze the drought spatiotemporal distribution traits under upcoming climate scenarios. The results showed that, under future climate conditions, SPEI values in most areas of the Huang-Huai-Hai region would continuously increase year by year, and drought would be alleviated to some extent at the same pace. However, with the increase of greenhouse gas concentration in the emission scenarios, SPEI values continued to decline. In the RCP8.5 scenario, the area of severe drought was large. To sum up, in the future climate scenario, the degree of drought in the Huang-Huai-Hai region will be alleviated to some extent with the increase of rainfall, but with the increase of greenhouse gas concentration, the degree of drought will be further intensified, posing a huge challenge to agricultural water use in the region. This study provides a theoretical foundation for alleviating drought in the Huang-Huai-Hai region in future climate scenarios.

In Canada, climate change is expected to increase the extreme precipitation events by magnitude and frequency, leading to more intense and frequent river flooding. In this study, we attempt to map the flood hazard and damage under projected climate scenarios (2050 and 2080). The study was performed in the two most populated municipalities of the Petite Nation River Watershed, located in southern Quebec (Canada). The methodology follows a modelling approach, in which climate projections are derived from the Hydroclimatic Atlas of Southern Quebec following two representative concentration pathways (RCPs) scenarios, i.e., RCP 4.5 and RCP 8.5. These projections are used to predict future river flows. A frequency analysis was carried out with historical data of the peak flow (period 1969–2018) to derive different return periods (2, 20, and 100 years), which were then fed into the GARI tool (Gestion et Analyse du Risque d’Inondation). This tool is used to simulate flood hazard maps and to quantify future flood risk changes. Projected flood hazard (extent and depth) and damage maps were produced for the two municipalities under current and for future scenarios. The results indicate that the flood frequencies are expected to show a minor decrease in peak flows in the basin at the time horizons, 2050 and 2080. In addition, the depth and inundation areas will not significantly change for two time horizons, but instead show a minor decrease. Similarly, the projected flood damage changes in monetary losses are projected to decrease in the future. The results of this study allow one to identify present and future flood hazards and vulnerabilities, and should help decision-makers and the public to better understand the significance of climate change on flood risk in the Petite Nation River watershed.

Earth–air heat exchanger (EAHE) potential was evaluated for nine locations representing a range of North American climates to determine the impact of future climate scenarios. This was motivated by the intrinsic relationship between climate conditions and EAHE potential. Future weather files were created using general circulation model (GCM) outputs for three representative concentration pathways (RCP) scenarios and two separate timeframes. These were used in a climate-based approach for estimating EAHE potential based on their capacity to provide either heating or cooling. The results demonstrated, cooling capacity of a hypothetical EAHE system decreased and heating capacity increased due to changes in demand. The largest effects were observed for later time periods and more drastic changes to radiative forcing. From the chosen locations, heating dominated sites are most susceptible to unfavorable changes in cooling capacity due to the synchronous timing of changes in cooling capacity relative to peak demand. This work highlights the importance of conducting a climate change impact assessment on EAHE potential in these climates to ensure system effectiveness into the future. Further work is required to explore the impact of depth and efficiency in addition to the resolution of climate changes models used.

Understanding the response of terrestrial ecosystems to future climate changes would substantially contribute to the scientific assessment of vegetation–climate interactions. Here, the spatiotemporal distribution and dynamics of vegetation in China were projected and compared based on comprehensive sequential classification system (CSCS) model under representative concentration pathway (RCP) RCP2.6, RCP4.5, and RCP8.5 scenarios, and five sensitivity levels were proposed. The results show that the CSCS model performs well in simulating vegetation distribution. The number of vegetation types would increase from 36 to 40. Frigid–perhumid rain tundra and alpine meadow are the most distributed vegetation types, with an area of more than 78.45 × 104 km2, whereas there are no climate conditions suitable for tropical–extra-arid tropical desert in China. Some plants would benefit from climate changes to a certain extent. Warm temperate–arid warm temperate zone semidesert would expand by more than 1.82% by the 2080s. A continuous expansion of more than 18.81 × 104 km2 and northward shift of more than 124.93 km in tropical forest would occur across all three scenarios. However, some ecosystems would experience inevitable changes. More than 1.33% of cool temperate–extra-arid temperate zone desert would continuously shrink. Five sensitivity levels present an interphase distribution. More extreme scenarios would result in wider ecosystem responses. The evolutionary trend from cold–arid vegetation to warm–wet vegetation is a prominent feature despite the variability in ecosystem responses to climate changes.