Downscaling of environmental indicators: A review.

Environmental indicators at different scales are important for environmental management, daily life, and scientific research. Because of the lack of statistics below a national scale for many environmental indicators, scholars have developed various downscaling methods to obtain finer-scale and diverse forms of data for different environmental indicators. However, the existing downscaling methods for environmental indicators are diverse and fragmented. Here, we reviewed the downscaling methods by reclassifying the environmental indicators from a life cycle perspective into five categories: natural resources use and related attributes; material and energy consumption; environmental discharge; climate change; and environmental footprints. We first provide a general introduction to downscaling theory in the environmental field, including definitions, techniques, and evolution. We then elaborate on downscaling methods and make an inventory of the five categories of environmental indicators. We summarize the downscaling methods commonly applied to specific indicators, scale transformation, the strengths and limitations of corresponding methods, and provide specific examples. Next, we discuss ways to select or construct downscaling methods based on four principles: objective orientation, data accessibility, model feasibility, and model adjustment. Finally, we explore the future direction of downscaling and provide insights for improving downscaling for environmental indicators. In this review, we generalize and clarify the downscaling techniques for environmental indicators, which will help facilitate the appropriate selection of downscaling methods by researchers.

Uncovering the risk spillover of agricultural water scarcity by simultaneously considering water quality and quantity.

Not only insufficient water quantity but also inadequate water quality can pose constraints on agricultural production and result in potential economic losses. Such economic losses in agriculture may adversely impact downstream producers through reduced input supplies. In this study, we developed an index assessing potential economic losses in agriculture under both quantity- and quality-induced water scarcity, termed integrated Agricultural Water Scarcity Risk (AWSR). Combining integrated AWSR with a multi-regional input-output model, we estimated the spillovers of integrated AWSR along supply chains. Our results showed that the intersectoral transmission of virtual integrated AWSR (sectoral spillovers in terms of integrated AWSR) were 5 times the virtual quantity-based AWSR. Pollution significantly intensifies the indirect supply-chain repercussions of agricultural water scarcity. Moreover, we identified some primary virtual integrated AWSR exporters (e.g., Jiangsu-vegetables and Shandong-swine, of which the integrated AWSR had considerable spillover effects on downstream sectors) and importers (e.g., Henan-chemical industry and Henan-textiles, which were vulnerable to upstream integrated AWSR), that could not be detected in quantity-based AWSR results. This study underscores the importance of water quality in the assessments of AWSR. Strategies to mitigate the spillovers of AWSR might be inefficient without the consideration of water quality.

Pollution exacerbates interregional flows of virtual scarce water driven by energy demand in China.

Existing studies on the virtual scarce water flows within the water-energy context focus on water quantity while largely ignoring water quality. This study improves the quantification method of scarce water uses by considering both blue water (representing water quantity) and grey water (indicating water quality). Based on a scarce-water extended multi-regional input-output model, we investigate the virtual scarce water flows driven by energy demand across 31 Chinese regions in 2012 and 2017. The results show that considering water quality provides new insights into the patterns of interregional flows of virtual scarce water driven by energy demand. The virtual integrated scarce water (VISW) flows, which consider both water quantity and quality, are 5 times the volume of virtual quantity-based scarce water (VQSW) flows. Moreover, certain regions (e.g., Hebei) are recognized as net VISW exporters, but are net importers in terms of VQSW. There are significant differences in the critical interregional pairs identified based on net VISW flows (e.g., Shandong-Zhejiang, and Shandong-Guangdong) and net VQSW flows (e.g., Heilongjiang-Guangdong, and Liaoning-Shaanxi). To reduce water scarcity based on the combined effect of both quantity and quality, the critical VISW interregional pairs should enhance cooperation through compensation payments and interregional technology transfer. This study highlights the importance of water quality in the assessment of virtual scarce water uses. Employing virtual scarce water as a policy tool to mitigate water scarcity might fail without the consideration of water quality.

Analysis of urban carbon metabolism characteristics based on provincial input-output tables.

To identify the key contributors of urban carbon emissions as well as the acting paths, it is necessary to analyze the carbon flows from a systematic perspective. Thus, the concept of urban carbon metabolism was introduced in this paper and correspondingly input-output analysis (IOA) and ecological network analysis (ENA) were combined to conduct the carbon metabolism analysis. Concretely speaking, the urban IO table was compiled based on the provincial one and then the direct and embodied urban carbon flows were accounted. Subsequently, the carbon metabolic network model was established, through which the characteristics of the metabolic network were further analyzed to better reveal the contributors and influencing factors of carbon emissions. Dongguan, a city famous as the "factory of the world", was chosen as the case. The results indicate that the total direct and embodied carbon flows were mainly concentrated in manufacture. Manufacture was found to be major factors affecting other compartments through indirect interplay. A trophic hierarchical structure was found, where compartments can be classified into primary producers, secondary producers, primary consumers and secondary consumers according to their metabolic characteristics in use of energy. Electricity, gas & water were defined as secondary producer, and its self-induced carbon flows accounted for more than 95% of the carbon flow conversion within this compartment. By further comparing the metabolic characteristics in Dongguan with that of Guangdong Province and other cities, measures were suggested to heighten energy utilization efficiency and promote positive interactions among compartments to promote the carbon emission reduction in Dongguan.