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].
Keywords: Building sector, Energy consumption, Emission reduction, City
Specifications Table
| Subject area | Energy |
| More specific subject area | Renewable Energy, Sustainability and the Environment |
| Type of data | Table, figure, text file |
| How data was acquired | Data were acquired from the provincial/city statistics, government development plans and a building energy downscaling model. |
| Data format | Analyzed and raster in ASCII form |
| Experimental factors | The raw data collected were organized in spreadsheets and then be calibrated to base-year data. Cities were grouped into four classes according to their emission intensity and economic development. |
| Experimental features | Spatial downscaling and scenario comparisons |
| Data source location | Hubei Province, China. |
| Data accessibility | Data are provided in the article |
| Related research article | Chen, H., Chen, W., 2019. Carbon mitigation of China's building sector on city-level: pathway and policy implications by a low-carbon province case study. J. Clean. Prod. 224, 207–217. |
Value of the Data
|
1. Data
The data consist of key parameters and assumptions required by modeling future evolutions of city- and grid-level building energy consumption [1]. Table 1 listed province-level building energy consumption of three sub-sectors by fuel type, in which data of 2015 is collected from energy statistical yearbook and the values of 2020–2030 are predicted by the combination of trend analysis concerning total energy consumption and its structure. To be more specific, total amount is specified by provincial mid-term energy development and emission control targets, while the demand increase rate of individual energy type is forecasted based on temporal extrapolation of historical records. When it comes to city-level carbon emission projections under different scenarios (Table 2), the relationship between historical socioeconomic data (Supplementary Material Table 1) and energy use intensity is drawn first, then future trajectory is estimated according to the characteristics of base and policy scenarios (Supplementary Material Table 2). Energy use intensities under S1 (base scenario), are assumed to be driven only by socioeconomic factors. And policy scenario S2 and S3 take into account electricity and renewable energy share increases and higher efficiency of building energy technologies, respectively. Parameters regarding clean energy proportions and efficiency improvement of the province were set based on literature review [[2], [3], [4], [5]]. Then these parameters were adjusted for different groups of cities according to their socioeconomic development status. Additionally, natural gas consumption was used as an example to display grid-level energy data (ASCII file in the Supplementary material), which was generated by spatial downscaling from city-level calculations.
Table 1.
Evolution of building energy consumption of Hubei Province during 2015–2030 modeled under S1 (104 tce).
| Coal | Oil | Natural Gas | Electricity | ||
|---|---|---|---|---|---|
| Urban | 2015 | 154.88 | 66.91 | 82.13 | 226.45 |
| 2020 | 147.79 | 71.18 | 155.17 | 292.98 | |
| 2025 | 114.78 | 95.11 | 253.21 | 457.88 | |
| 2030 | 88.01 | 97.69 | 341.75 | 589.15 | |
| Rural | 2015 | 255.24 | 70.020 | 5.22 | 116.39 |
| 2020 | 304.82 | 81.86 | 9.70 | 134.28 | |
| 2025 | 286.95 | 116.24 | 15.83 | 173.34 | |
| 2030 | 242.02 | 126.42 | 21.36 | 200.31 | |
| Commercial | 2015 | 350.52 | 75.04 | 85.90 | 248.38 |
| 2020 | 351.01 | 85.42 | 166.81 | 292.98 | |
| 2025 | 286.95 | 110.96 | 300.69 | 425.18 | |
| 2030 | 234.69 | 109.18 | 427.19 | 589.15 |
Table 2.
Direct carbon emissions (Mt CO2) from the building sector of 17 prefectural cities in Hubei Province under different scenarios.
| S1 |
S2 |
S3 |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2015 | 2020 | 2025 | 2030 | 2015 | 2020 | 2025 | 2030 | 2015 | 2020 | 2025 | 2030 | |
| WH | 6.62 | 8.18 | 9.66 | 10.57 | 6.62 | 7.21 | 7.20 | 7.07 | 6.62 | 6.49 | 6.12 | 5.65 |
| HS | 1.09 | 1.20 | 1.31 | 1.31 | 1.09 | 1.16 | 1.23 | 1.20 | 1.09 | 1.11 | 1.11 | 1.01 |
| SY | 1.47 | 1.62 | 1.73 | 1.66 | 1.47 | 1.58 | 1.63 | 1.54 | 1.47 | 1.50 | 1.47 | 1.30 |
| YC | 2.05 | 2.52 | 2.85 | 3.08 | 2.05 | 2.34 | 2.42 | 2.45 | 2.05 | 2.18 | 2.12 | 2.03 |
| XY | 2.57 | 3.09 | 3.50 | 3.65 | 2.57 | 2.88 | 2.98 | 2.91 | 2.57 | 2.68 | 2.61 | 2.41 |
| EZ | 0.56 | 0.55 | 0.61 | 0.61 | 0.56 | 0.52 | 0.53 | 0.50 | 0.56 | 0.48 | 0.47 | 0.42 |
| JM | 1.25 | 1.38 | 1.51 | 1.55 | 1.25 | 1.34 | 1.41 | 1.41 | 1.25 | 1.27 | 1.26 | 1.19 |
| XG | 2.14 | 2.29 | 2.36 | 2.23 | 2.14 | 2.22 | 2.21 | 2.04 | 2.14 | 2.11 | 1.99 | 1.72 |
| JZ | 2.45 | 2.58 | 2.61 | 2.42 | 2.45 | 2.55 | 2.53 | 2.31 | 2.45 | 2.47 | 2.32 | 2.00 |
| HG | 2.87 | 3.31 | 3.31 | 2.98 | 2.87 | 3.27 | 3.22 | 2.87 | 2.87 | 3.17 | 2.95 | 2.47 |
| XN | 1.05 | 1.18 | 1.26 | 1.23 | 1.05 | 1.15 | 1.18 | 1.12 | 1.05 | 1.09 | 1.06 | 0.94 |
| SZ | 0.99 | 1.10 | 1.18 | 1.13 | 0.99 | 1.08 | 1.15 | 1.09 | 0.99 | 1.05 | 1.05 | 0.94 |
| ES | 1.73 | 1.98 | 1.97 | 1.74 | 1.73 | 1.93 | 1.87 | 1.61 | 1.73 | 1.83 | 1.68 | 1.36 |
| XT | 0.58 | 0.65 | 0.74 | 0.75 | 0.58 | 0.64 | 0.71 | 0.72 | 0.58 | 0.62 | 0.65 | 0.62 |
| QJ | 0.54 | 0.60 | 0.65 | 0.65 | 0.54 | 0.59 | 0.60 | 0.59 | 0.54 | 0.56 | 0.54 | 0.50 |
| TM | 0.40 | 0.45 | 0.49 | 0.49 | 0.40 | 0.45 | 0.47 | 0.46 | 0.40 | 0.43 | 0.43 | 0.40 |
| SNJ | 0.03 | 0.04 | 0.04 | 0.04 | 0.03 | 0.04 | 0.04 | 0.03 | 0.03 | 0.04 | 0.03 | 0.03 |
Note:
WH: Wuhan; HS: Huangshi; SY: Shiyan; YC: Yichang; XY: Xiangyang; EZ: E'zhou; JM: Jinmen; XG: Xiaogan; JZ: Jinzhou.
HG: Huanggang; XN: Xianning; SZ: Suizhou; ES: Enshi; XT: Xiantao; QJ: Qianjiang; TM: Tianmen; SNJ: Shennongjia.
2. Experimental design, materials, and methods
The data presented in this article aim to provide future building energy consumption and emissions with higher resolution, which could be applied to climate modeling and more refined low-carbon management. The dataset incorporates information on three geographic/administrative levels and is produced by using the systematic downscaling framework described in Ref. [6]. First, provincial statistics [7] were employed to identify the historical trends of total energy demand and the share of the building sector in it. Then city-level energy statistics such as household and commercial natural gas and electricity use were used to describe the distribution patterns of building energy use intensity by quantitatively linking them to socioeconomic parameters. These trend analyses derived from past data could then be used to model the spatial and temporal variations of future building energy demand for the province. More specifically, grid-level urban/rural population and Gross Domestic Production (GDP) were incorporated into these relationships to generate spatial proxies for energy consumption distributions within cities. The above process could be generalized as shown in Fig. 1.
Fig. 1.
General workflow chart of data preparation.
Acknowledgments
The authors would like to thank the support from National Natural Science Foundation of China (No. 51861135102, 71690243) and Project funded by China Postdoctoral Science Foundation (2017M620808).
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104952.
Contributor Information
Han Chen, Email: chenhan1989@mail.tsinghua.edu.cn.
Wenying Chen, Email: chenwy@mail.tsinghua.edu.cn.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Chen H., Chen W.Y. Modelling building's decarbonization with application of China TIMES model. J. Clean. Prod. 2019;224:207–217. [Google Scholar]
- 2.IEA . 2015. Building Energy Use in China: Transforming Construction and Influencing Consumption to 2050. IEA Publication in Collaboration with Tsinghua University Building Energy Research Center (BERC)https://www.iea.org/publications/freepublications/publication/PARTNERCOUNTRYSERIESBuildingEnergy_WEB_FINAL.pdf [Google Scholar]
- 3.Shi J.C., Chen W.Y., Yin X. Modelling building's decarbonization with application of China TIMES model. Appl. Energy. 2016;162:1303–1312. [Google Scholar]
- 4.Zhou N., Fridley D., McNeil M., Zheng N., Ke J., Levine M. China's Energy and Carbon Emissions Outlook to 2050. Reports from Lawrence Berkeley National Laboratory (LBNL) https://china.lbl.gov/sites/all/files/lbl-4472e-energy-2050april-2011.pdf
- 5.Yu S., Eom J., Evans M., Clarke L. A long-term, integrated impact assessment of alternative building energy code scenarios in China. Energy Policy. 2014;67:626–639. [Google Scholar]
- 6.Chen H., Chen W.Y. Potential impact of shifting coal to gas and electricity for building sectors in 28 major northern cities of China. Appl. Energy. 2019;236:1049–1106. [Google Scholar]
- 7.Hubei Statistics Bureau. Hubei Statistical Yearbook 2001-2018. China Statistics Press, Beijing.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.