Dataset Information

Projection datasets of city- and grid-level building energy consumption for Hubei Province, China.

ABSTRACT: This data article takes a typical low-carbon pilot province in Middle China (Hubei) as an example to present the pathway of building energy conservation and emission reductions for different cities. The data contains middle-to-long-term predictions of provincial socioeconomic factors (Gross Regional Production, population and urbanization rate), based on which building sector energy consumption under the base scenario could be estimated on provincial scale. Besides, energy demand and structures of the building sectors in cities from various categories are also provided by considering the spatial heterogeneity of city-level economic development and energy use intensities. This dataset could be used to calculate building sector emission reduction potentials on city scale so as to fill in the research gap of mitigation pathway modeling for multiple cities. Moreover, it also proposes a reasonable and convenient approach to allocate provincial targets concerning emission intensity and total amount control. Finally, the data offers high-resolution gridded projections for building energy consumption, which could be expanded to other sectors and cities to assist in more refined urban governance and atmospheric and climate modeling. The data presented herein are associated with the research article "Carbon mitigation of China's building sector on city-level: pathway and policy implications by a low-carbon province case study" [1].

SUBMITTER: Chen H

PROVIDER: S-EPMC6921097 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Environmental assessment of eutrophication or heavy metals in urban lakes is an important reference for identifying the pollution degree and formulating pollution prevention strategies. At present, the most research on lake health states is often evaluated from a single angle for toxic metals pollution or eutrophication using the standard comparison method for both, the comprehensive trophic level index (TLI), and the health risk assessment for toxic metals. Moreover, the above deterministic methods probably lead to biased or unreliable assessment due to the randomness and fuzziness in environment system caused by natural change and human activities. In this paper, a fuzzy comprehensive lake health assessment method (FCLHAM) was established to evaluate comprehensive lake health states more comprehensively and accurately, which integrates quantitative eutrophication and health risk considerations. To test and verify FCLHAM, 21 lakes, scientifically selected from the total 143 lakes in the Chinese Wuhan city as study case, were investigated and analyzed for their state of eutrophication and the health risk posed by heavy metals. According to the FCLHAM, the average comprehensive lake health state decreased in the sequence of L20 (considerate risk level) > L1⁻L17, L19, L21 (moderate risk level) > L18 (low risk level). Based on the result, lakes were classified into three categories: general management (L18), enhanced management (L1⁻L17, L19, L21), and priority management (L20). If the 143 lakes in Wuhan were classified by the "area-region-function" classification, they would be assigned to the same category as the representative lakes of the same type. At this point, we will attribute all of Wuhan's lakes to the three types. Depending on the characteristics of each type, a targeted approach to different types of management for each type of lake is a more efficient way to manage many of Wuhan's lakes. This management mode also serves as an effective reference for the environmental management of urban lakes both at home and abroad. In other words, according to the FCLHAM, a hierarchical management system based on lake characteristics classification was obtained.

Dataset Information

Projection datasets of city- and grid-level building energy consumption for Hubei Province, China.

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