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Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for the Mongolian Plateau and their climatic determinants. To do so, we derived three phenological metrics from remotely sensed vegetation indices and associated these with climate data for the period of 1982 to 2011. The results suggested that the start of the growing season advanced by 0.10 days yr-1, the end was delayed by 0.11 days yr-1, and the length of the growing season expanded by 6.3 days during the period from 1982 to 2011. The delayed end and extended length of the growing season were observed consistently in grassland, forest, and shrubland, while the earlier start was only observed in grassland. Partial correlation analysis between the phenological events and the climate variables revealed that higher temperature was associated with an earlier start of the growing season, and both temperature and precipitation contributed to the later ending. Overall, our findings suggest that climate change will substantially alter the vegetation phenology in the grasslands of the Mongolian Plateau, and likely also in biomes with similar environmental conditions, such as other semi-arid steppe regions.

The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress to freshwater resources. Ensuring that increases in irrigated production only occur in places where water is relatively abundant is a key objective of sustainable agriculture, and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet a spatially detailed understanding of the evolution of global area equipped for irrigation (AEI) is missing. Here we utilize the latest sub-national irrigation statistics (covering 17298 administrative units) from various official sources to develop a gridded (5 arc-min resolution) global product of AEI for the years 2000, 2005, 2010, and 2015. We find that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha) with locations of both substantial expansion (e.g., northwest India, northeast China) and decline (e.g., Russia). Combining these outputs with information on green (i.e., rainfall) and blue (i.e., surface and ground) water stress, we also examine to what extent irrigation has expanded unsustainably (i.e., in places already experiencing water stress). We find that more than half (52%) of irrigation expansion has taken place in regions that were already water stressed, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security. Recommended citation: Mehta, P., Siebert, S., Kummu, M. et al. Half of twenty-first century global irrigation expansion has been in water-stressed regions. Nat Water (2024). https://doi.org/10.1038/s44221-024-00206-9 Open-access peer reviewed publication available at https://www.nature.com/articles/s44221-024-00206-9 Files G_AEI_*.ASC were produced using the GMIA dataset[https://data.apps.fao.org/catalog/iso/f79213a0-88fd-11da-a88f-000d939bc5d8]. Files MEIER_G_AEI_*.ASC were produced using Meier et al. (2018) dataset [https://doi.pangaea.de/10.1594/PANGAEA.884744].

In this study, our aim was to explore the potential energy savings obtainable from the recycling of 1 tonne of Construction and Demolition Waste (C&DW) generated in the Metropolitan City of Naples. The main fraction composing the functional unit are mixed C&DW, soil and stones, concrete, iron, steel and aluminium. The results evidence that the recycling option for the C&DW is better than landfilling as well as that the production of recycled aggregates is environmentally sustainable since the induced energy and environmental impacts are lower than the avoided energy and environmental impacts in the life cycle of recycled aggregates. This LCA study shows that the transition to the Circular Economy offers many opportunities for improving the energy and environmental performances of the construction sector in the life cycle of construction materials by means of internal recycling strategies (recycling C&DW into recycled aggregates, recycled steel, iron and aluminum) as well as external recycling by using input of other sectors (agri-food by-products) for the manufacturing of construction materials. In this way, the C&D sector also contributes to realizing the energy and bioeconomy transition by disentangling itself from fossil fuel dependence.

AbstractA fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

The basic priority to neutralize carbon emissions (CE) is to achieve the sustainable development goal of climate action. In this regard, the role of renewable energy (RE) is widely debated. Transition to RE and environment-related innovation in technologies (ERIT) is a need of the hour. However, the challenge of uncertain economic policies in the transition process is interesting to study. Therefore, the present study intends to add a value to the literature by re-examining the interactive effect of economic policy uncertainty (EPU) in RE and ERIT and the transition process towards carbon neutrality. The second-generation econometric methodology is applied to empirically test the proposed interactive linkage of EPU, RE, ERIT, and CE. Findings reported the negative role of EPU in adopting RE and ERIT in seven emerging economies: Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. However, the ERIT and RE are found to be supportive of neutralizing the CE. Emerging seven (E7) countries are suggested to be consistent in their economic policies to nurture the transition process toward a sustainable environment.

The unprecedented economic growth in recent decades has cultivated the exploitation of natural resources and over-consumption, leading to ecological deterioration and sustainability. The ever-increasing consumption in developing countries is creating a significant environmental strain. Thus, the industry and consumers’ environmental issues and their harmful effects on human health have led to concerns among researchers, scientists, academic communities, and policymakers. The present work examines the impact of different consumption value factors on sustainable consumption behavior concerning consumer choice in Pakistan and China. A cross-sectional study is conducted, and data are collected through a primary source questionnaire. A sample of 431 respondents is chosen from different cities in Pakistan, and a sample of 342 respondents is selected from China. Estimation techniques like descriptive statistics, frequency distribution, multicollinearity, R square, independent sample t-test, the coefficient of correlation, and regression analysis are used for the data analysis. The comparative results show that knowledge values (KVs) and emotional values (EMVs) significantly influence the choice behavior of respondents toward environmentally friendly products both in Pakistan and China. In contrast, social values (SVs) and conditional values (CVs) show insignificant influence. Furthermore, functional values (FVs) are significant in Pakistan while insignificant in the context of China, and environmental values (EVs) are significant in China although insignificant in Pakistan with regard to sustainable consumption behavior.

Global climate change, extreme climate events, earthquakes and their accompanying natural disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, strategic interactions and complex structure of the Earth system, the understanding and in particular the predicting of such disruptive events represent formidable challenges for both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge for a better understanding of the Earth system, including climate extreme events, earthquakes and Earth geometric relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as, critical phenomena, network theory, percolation, tipping points analysis, as well as entropy can be applied to complex Earth systems (climate, earthquakes, etc.). Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics approaches can be useful in the field of Earth system science.