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Abstract: In this study, a large-scale integrated modeling system (IMS) was developed for supporting climate-change impact analysis and adaptation planning under multi-level uncertainties. A number of methodologies were seamlessly incorporated within IMS, including fuzzy-interval inference method (FIIM), inexact energy model (IEM), and uncertainty analysis. The system could (i) encompass multiple technologies, energy resources, and sub-sectors, and climate change impact analysis into a general modeling framework, (ii) address interactions of climate change impacts on multiple energy sub-sectors and resources within an EMS, (iii) identify optimal adaptation strategies of an EMS to climate change impact through a two-step procedure, (iv) deal with multiple levels of uncertain information associated with processes of climate change impact analysis and adaptation planning, and (v) seamlessly combine climate change impact analysis results with inexact adaptation planning. It could provide decision makers a comprehensive view on the EMS as well as the corresponding adaptation schemes under climate change, greatly improving the robustness and completeness of the decision-making processes. The generated solutions can provide desired energy resource/service allocation with a minimized system cost, a maximized system reliability and a maximized energy security under varied climate change impact levels, as well as multiple levels of uncertainties. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the Province of Manitoba, Canada. [Copyright &y& Elsevier]

Bioenergy is considered a promising alternative to achieve carbon neutrality and emission reduction. There is a debate over the role of bioenergy in climate change mitigation because of carbon emissions from the production and utilization of biofuels. Recently, studies on emissions of biogenic volatile organic compounds (BVOCs) have substantially increased as they have a significant impact on human health and climate change due to large emissions and high reactivity. A scientometric analysis of 2895 journal papers during 1991 and 2020 related to BVOC emissions from the Web of Science Core Collection was conducted by CiteSpace and Carrot2. Results showed that three main knowledge domains were BVOC emissions and drivers, BVOCs and their oxidation products in biosphere-atmosphere exchange, and soil BVOCs in plant-microbe interactions during the period under study. Generally, the relationships between plant BVOC emissions and environmental variables (e.g., temperature, drought, light solar radiation, humidity, availability of nutrients, carbon dioxide, ozone, etc.) can be investigated by measurement techniques (i.e., chamber) or models (i.e., Model of Emissions of Gases and Aerosols from Nature). Soils could work as both the sink and source of BVOCs through various processes that are affected by soil temperatures, pH, organic matter, and moisture. Anthropogenic activities might affect the emission of BVOCs and promote the formation of BVOC-derived pollutants (e.g., ozone, particles, etc.). Incorporating BVOC emissions and their relationships with pollutants, biomass, and energy into environmental management and decision-making is a promising direction. [Display omitted] • A scientometric analysis was conducted on the research evolution of BVOC emissions. • Ten clusters generated by CiteSpace were logically reclassified into three hotspots. • MEGAN is the most frequently empirical model for BVOC emission assessment. • BVOC-derived pollutants like SOA are likely affected by anthropogenic activities. • Soils in terrestrial ecosystems can act as both the source and sink of BVOCs. [ABSTRACT FROM AUTHOR]