Keeping global warming within 1.5 °C reduces future risk of yield loss in the United States: A probabilistic modeling approach

Risk of Crop Yield Reduction in China under 1.5 °C and 2 °C Global Warming from CMIP6 Models

Warmer temperatures significantly influence crop yields, which are a critical determinant of food supply and human well-being. In this study, a probabilistic approach based on bivariate copula models was used to investigate the dependence (described by joint distribution) between crop yield and growing season temperature (T_GS) in the major producing provinces of China for three staple crops (i.e., rice, wheat, and maize). Based on the outputs of 12 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathway 5-8.5, the probability of yield reduction under 1.5 °C and 2 °C global warming was estimated, which has great implications for agricultural risk management. Results showed that yield response to T_GS varied with crop and region, with the most vulnerable being rice in Sichuan, wheat in Sichuan and Gansu, and maize in Shandong, Liaoning, Jilin, Nei Mongol, Shanxi, and Hebei. Among the selected five copulas, Archimedean/elliptical copulas were more suitable to describe the joint distribution between T_GS and yield in most rice-/maize-producing provinces. The probability of yield reduction was greater in vulnerable provinces than in non-vulnerable provinces, with maize facing a higher risk of warming-driven yield loss than rice and wheat. Compared to the 1.5 °C global warming, an additional 0.5 °C warming would increase the yield loss risk in vulnerable provinces by 2-17%, 1-16%, and 3-17% for rice, wheat, and maize, respectively. The copula-based model proved to be an effective tool to provide probabilistic estimates of yield reduction due to warming and can be applied to other crops and regions. The results of this study demonstrated the importance of keeping global warming within 1.5 °C to mitigate the yield loss risk and optimize agricultural decision-making in vulnerable regions.

Review: biological engineering for nature-based climate solutions

Nature-based Climate Solutions are landscape stewardship techniques to reduce greenhouse gas emissions and increase soil or biomass carbon sequestration. These mitigation approaches to climate change present an opportunity to supplement energy sector decarbonization and provide co-benefits in terms of ecosystem services and landscape productivity. The biological engineering profession must be involved in the research and implementation of these solutions-developing new tools to aid in decision-making, methods to optimize across different objectives, and new messaging frameworks to assist in prioritizing among different options. Furthermore, the biological engineering curriculum should be redesigned to reflect the needs of carbon-based landscape management. While doing so, the biological engineering community has an opportunity to embed justice, equity, diversity, and inclusion within both the classroom and the profession. Together these transformations will enhance our capacity to use sustainable landscape management as an active tool to mitigate the risks of climate change.

Using AI-MCDM Model to Boost Sustainable Energy System Development: A Case Study on Solar Energy and Rainwater Collection in Guangdong Province

Rural areas in southern China receive ample rainfall annually as well as over 1600 h of annual sunshine. Despite a generally severe urban–rural development imbalance, these rural areas feature well-developed basic infrastructure and diverse economic activities. Rural revitalization policies in these areas have emphasized the development of cultural and ecological tourism, which has spurred economic development and given rise to a trend of villa construction. Residential buildings sit on large areas where natural resources are abundant. These advantages are conducive to the development and use of sustainable resources. This study proposes an incentive policy encouraging rural residents to renovate their buildings to include rainwater conservation and solar power generation. The Delphi method, an analytic hierarchy process, and fuzzy logic theory were combined to establish an AI-MCDM model, with applications of artificial intelligence and multiple-criteria decision making. Using Conghua District, Guangdong Province as an example, the study suggested that the model is beneficial to increasing the willingness of rural residents to reconstruct and renovate their residences, promoting the development of a low-carbon ecological region, Wenquan Township. We conducted the Delphi process twice to assess and validate incentives for installing natural resource conservation structures in agricultural areas. Nine criteria were identified, which can be divided into three main dimensions of participation situation, generating capacity, and storage facilities. The proposed AI-MCDM model developed using the Delphi–Fuzzy Analytic Hierarchy Process Model has high objectivity and can support rural areas in developing low-carbon, sustainable characteristics. The findings can serve as a reference for governments formulating incentives to encourage the installation of rainwater conservation and solar energy generation structures by rural households.

Higher Temperatures Decrease Fruit Size in Strawberry Growing in the Subtropics

Five strawberry (Fragaria × ananassa Duch.) cultivars were grown in Queensland, Australia to determine whether higher temperatures affect production. Transplants were planted on 29 April and data collected on growth, marketable yield, fruit weight and the incidence of small fruit less than 12 g until 28 October. Additional data were collected on fruit soluble solids content (SSC) and titratable acidity (TA) from 16 September to 28 October. Minimum temperatures were 2 °C to 4 °C higher than the long-term averages from 1965 to 1990. Changes in marketable yield followed a dose-logistic pattern (p < 0.001, R2s = 0.99). There was a strong negative relationship between fruit weight (marketable) and the average daily mean temperature in the four or seven weeks before harvest from 29 July to 28 October (p < 0.001, R2s = 0.90). There were no significant relationships between SSC and TA, and temperatures in the eight days before harvest from 16 September to 28 October (p > 0.05). The plants continued to produce a marketable crop towards the end of the season, but the fruit were small and more expensive to harvest. Higher temperatures in the future are likely to affect the economics of strawberry production in subtropical locations.

An overview on trace CO2 removal by advanced physisorbent materials

This review paper focuses on various gas processing technologies and materials that efficiently capture trace levels of carbon dioxide (CO₂). Fundamental separation mechanisms such as absorption, adsorption, and distillation technology are presented. Liquid amine-based carbon capture (C-capture) technologies have been in existence for over half a century, however, liquid amine capture relies upon chemical reactions and is energy-intensive. Liquid amines are thus not economically viable for broad deployment and offer little room for innovation. Innovative C-capture technologies must improve both the environmental footprint and cost-effectiveness. As a promising alternative, physisorbents have many advantages including considerably lower regeneration energy. Generally, existing classes of physisorbent materials, such as metal-organic frameworks (MOFs) and zeolites are selective toward C-capture. However, their selectivity is currently not high enough to remove trace levels (e.g., ~1%) of CO₂ from various natural gas process streams. This review summarizes the current advancements in physisorbent materials for CO₂ capture. Here, key performance parameters needed to select the most suitable candidate are highlighted. Furthermore, this review discusses the scope for the development of better performing CO₂ selective physisorbents from both environmental and economic perspectives. In addition, hybrid ultra microporous materials (HUMs), characterized mainly by ultra-micro pores (<0.7 nm), are discussed in reference to C-capture. Various characteristics of HUMs result in high selectivity and applicability in difficult separations such as the gas sweetening and C-capture from complex humid mixed gas streams.

Heatwave Trends and the Population Exposure Over China in the 21st Century as Well as Under 1.5 °C and 2.0 °C Global Warmer Future Scenarios

Heatwaves exert negative socio-economic impacts and particularly have serious effects on public health. Based on the multi-model ensemble (MME) results of 10 downscaled high-resolution Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) model output from NASA Earth Exchange Global Daily Downscaled Projections (NASA-GDDP), the intensity (largest lasting time), frequency and total duration of heatwaves over China as well as population exposure in the 21st century and at 1.5 °C and 2.0 °C above pre-industrial levels are investigated by using the three indices, the Heat Wave Duration Index (HWDI), annual total frequency of heatwaves (N_HW) and annual total days of heatwaves (T_HW) under RCP4.5 and RCP8.5. The MME results illustrate that heatwaves are projected to become more frequent (0.40/decade and 1.26/decade for N_HW), longer-lasting (3.78 days/decade and 14.59 days/decade for T_HW) as well as more extreme (1.07 days/decade and 2.90 days/decade for HWDI under RCP4.5 and RCP8.5 respectively) over China. High latitude and high altitude regions, e.g., the Tibetan Plateau and northern China, are projected to experience a larger increase of intensity, frequency and the total time of heatwaves compared with southern China (except Central China). The total population affected by heatwaves is projected to increase significantly and will reach 1.18 billion in later part of the 21st century, and there will be more and more people expected to suffer long heatwave time (T_HW) in the 21st century. Compared with a 2.0 °C global warming climate, holding the global warming below 1.5 °C can avoid 26.9% and 29.1% of the increase of HWDI, 34.7% and 39.64% for N_TW and 35.3%–40.10% of T_HW under RCP4.5 and RCP8.5 respectively. The half-degree less of warming will not only decrease the population exposure by 53–83 million but also avoid the threat caused by longer heatwave exposure under the two scenarios. Based on the comprehensive assessment of heatwave under the two RCP scenarios, this work would help to enhance the understanding of climate change and consequent risk in China and thus could provide useful information for making climate adaptation policies.

The Effect of Climate Change on Spring Maize (Zea mays L.) Suitability across China

Spring maize (Zea mays L.) is a thermophilic C4 crop which is sensitive to climate change. This paper provides a detailed assessment of the effect of climate change on the crop from a new perspective, by predicting the probability of the potential distribution of spring maize across China. The affected area of spring maize suitability was identified, and then the affected area was subdivided into the improved area and the deteriorated area. Our results confirmed that there was a detrimental consequence for spring maize suitability under observed climate change from 1961–1990 to 1981–2010. However, our results revealed that warming scenarios of 1.5 °C and 2 °C were helpful for the suitable area expansion of spring maize. The affected area was smaller under warming scenarios than under historical climate change, revealing that temperature rise alone was not enough to trigger a “tipping point” (a threshold value after which abrupt shifts occur) for spring maize, even if warming is 2 °C above the level of 1961–1990. Our results not only benefit China in the design of mitigation and adaptation strategies, but also provide a theoretical judgement that the impact of global warming on the crop ecosystem is not serious if other climate factors remain unchanged.