Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Future scenarios for energy consumption and carbon emissions due to demographic transitions in Chinese households


Population dynamics has been acknowledged as a key concern for projecting future emissions, partly because of the huge uncertainties related to human behaviour. However, the heterogeneous shifts of human behaviour in the process of demographic transition are not well explored when scrutinizing the impacts of population dynamics on carbon emissions. Here, we expand the existing population–economy–environment analytical structure to address the above limitations by representing the trend of demographic transitions to small-family and ageing society. We specifically accommodate for inter- and intra-life-stage variations in time allocation and consumption in the population rather than assuming a representative household, and take a less developed province, Sichuan, in China as the empirical context. Our results show that the demographic shift to small and ageing households will boost energy consumption and carbon emissions, driven by the joint variations in time-use and consumption patterns. Furthermore, biased pictures of changing emissions will emerge if the time effect is disregarded.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Analytical mechanism for demographic transition, lifestyle and environmental outputs.
Fig. 2: Chinese time-use pattern and consumption pattern.
Fig. 3: Four scenarios of future society in Sichuan.
Fig. 4: Structure of energy-demand and CO2-emissions change compared with S1 for Sichuan.
Fig. 5: Decomposition of CO2 emissions increase in scenario S4 in contrast to the base year 2009 for Sichuan.


  1. 1.

    Harper, S. Economic and social implications of aging societies. Science 346, 587–591 (2014).

    Article  Google Scholar 

  2. 2.

    Cohen, J. E. Human population: the next half century. Science 302, 1172–1175 (2003).

    Article  Google Scholar 

  3. 3.

    O’Neill, B. C. et al. Global demographic trends and future carbon emissions. Proc. Natl Acad. Sci. USA 107, 17521–17526 (2010).

    Article  Google Scholar 

  4. 4.

    Jiang, L. & Hardee, K. How do recent population trends matter to climate change? Popul. Res. Policy Rev. 30, 287–312 (2011).

    Article  Google Scholar 

  5. 5.

    Allcott, H. & Mullainathan, S. Behavior and energy policy. Science 327, 1204–1205 (2010).

    Article  Google Scholar 

  6. 6.

    Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C. & Vandenbergh, M. P. Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions. Proc. Natl Acad. Sci. USA 106, 18452–18456 (2009).

    Article  Google Scholar 

  7. 7.

    Rosa, E. A. & Dietz, T. Human drivers of national greenhouse-gas emissions. Nat. Clim. Change 2, 581–586 (2012).

    Article  Google Scholar 

  8. 8.

    Contestabile, M. Social sciences: Broadening energy research. Nat. Clim. Change 4, 420–420 (2014).

    Google Scholar 

  9. 9.

    Druckman, A., Buck, I., Hayward, B. & Jackson, T. Time, gender and carbon: A study of the carbon implications of British adults’ use of time. Ecol. Econ. 84, 153–163 (2012).

    Article  Google Scholar 

  10. 10.

    Ellegård, K. & Palm, J. Visualizing energy consumption activities as a tool for making everyday life more sustainable. Appl. Energy 88, 1920–1926 (2011).

    Article  Google Scholar 

  11. 11.

    Nässén, J. & Larsson, J. Would shorter working time reduce greenhouse gas emissions? An analysis of time use and consumption in Swedish households. Environ. Plann. C 33, 726–745 (2015).

    Article  Google Scholar 

  12. 12.

    Gørtz, M. Leisure, Household Production, Consumption and Economic Well-Being. PhD thesis, Univ. Copenhagen (2006).

  13. 13.

    Lee, R. How Population Aging Affects the Macroeconomy. In Econ. Policy Symp. (Federal Reserve Bank of Kansas City, Jackson Hole, 2014);

  14. 14.

    Menz, T. & Welsch, H. Population aging and carbon emissions in OECD countries: Accounting for life-cycle and cohort effects. Energy Econ. 34, 842–849 (2012).

    Article  Google Scholar 

  15. 15.

    Becker, G. S. A theory of the allocation of time. Econ. J. 75, 493-517 (1965).

    Article  Google Scholar 

  16. 16.

    Yang, Y., Zhao, T., Wang, Y. & Shi, Z. Research on impacts of population-related factors on carbon emissions in Beijing from 1984 to 2012. Environ. Impact Assess. Rev. 55, 45–53 (2015).

    Article  Google Scholar 

  17. 17.

    Fu, C., Wang, W. & Tang, J. Exploring the sensitivity of residential energy consumption in China: Implications from a micro-demographic analysis. Energy Res. Soc. Sci. 2, 1–11 (2014).

    Article  Google Scholar 

  18. 18.

    Pachauri, S. An analysis of cross-sectional variations in total household energy requirements in India using micro survey data. Energy Policy 32, 1723–1735 (2004).

    Article  Google Scholar 

  19. 19.

    O’Neill, B. C., Ren, X., Jiang, L. & Dalton, M. The effect of urbanization on energy use in India and China in the iPETS model. Energy Econ. 34, S339–S345 (2012).

    Article  Google Scholar 

  20. 20.

    O’Neill, B. C., Jiang, L. & Gerland, P. Plausible reductions in future population growth and implications for the environment. Proc. Natl Acad. Sci. USA 112, E506 (2015).

  21. 21.

    MacKellar, F. L., Lutz, W., Prinz, C. & Goujon, A. Population, households, and CO2 emissions. Popul. Dev. Rev. 21, 849–865 (1995).

  22. 22.

    Bin, S. & Dowlatabadi, H. Consumer lifestyle approach to US energy use and the related CO2 emissions. Energy Policy 33, 197–208 (2005).

    Article  Google Scholar 

  23. 23.

    Dalton, M., O’Neill, B., Prskawetz, A., Jiang, L. & Pitkin, J. Population aging and future carbon emissions in the United States. Energy Econ. 30, 642–675 (2008).

    Article  Google Scholar 

  24. 24.

    Melnikov, N. B., O’Neill, B. C. & Dalton, M. G. Accounting for household heterogeneity in general equilibrium economic growth models. Energy Econ. 34, 1475–1483 (2012).

    Article  Google Scholar 

  25. 25.

    Davis, S. J. & Caldeira, K. Consumption-based accounting of CO2 emissions. Proc. Natl Acad. Sci. USA 107, 5687–5692 (2010).

    Article  Google Scholar 

  26. 26.

    Liddle, B. & Lung, S. Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Popul. Environ. 31, 317–343 (2010).

    Article  Google Scholar 

  27. 27.

    O’Neill, B. C. & Chen, B. S. Demographic determinants of household energy use in the United States. Popul. Dev. Rev. 28, 53–88 (2002).

    Google Scholar 

  28. 28.

    Jalas, M. & Juntunen, J. K. Energy intensive lifestyles: Time use, the activity patterns of consumers, and related energy demands in Finland. Ecol. Econ. 113, 51–59 (2015).

    Article  Google Scholar 

  29. 29.

    Jalas, M. The everyday life context of increasing energy demands. J. Ind. Ecol. 9, 129–145 (2005).

    Article  Google Scholar 

  30. 30.

    Neuwirth, N. The Determinants of Activities within the Family: A SUR-Approach to Time Use Studies. Working Paper No. 59 (Austrian Institute for Family Studies, 2007)

  31. 31.

    Wang, D., Chai, Y. & Li, F. Built environment diversities and activity–travel behaviour variations in Beijing, China. J. Transp. Geogr. 19, 1173–1186 (2011).

    Article  Google Scholar 

  32. 32.

    Lee, R. D. & Mason, A. Population Aging and the Generational Economy: A Global Perspective (Edward Elgar Cheltenham, UK, Northampton, 2011).

  33. 33.

    National Bureau of Statistics China 2008 China Time Use Survey Data Compilation (China Statistics Press, Beijing, 2009).

  34. 34.

    Liu, Z. et al. Targeted opportunities to address the climate-trade dilemma in China. Nat. Clim. Change 6, 201–206 (2015).

    Article  Google Scholar 

  35. 35.

    Gruebler, A. Technology and Global Change (Cambridge Univ. Press, Cambridge, 1998).

  36. 36.

    World Population Prospects: The 2012 Revision (UN DESA, 2013).

  37. 37.

    Clayton, S. et al. Psychological research and global climate change. Nat. Clim. Change 5, 640–646 (2015).

    Article  Google Scholar 

  38. 38.

    Hamza, N. & Gilroy, R. The challenge to UK energy policy: An ageing population perspective on energy saving measures and consumption. Energy Policy 39, 782–789 (2011).

    Article  Google Scholar 

  39. 39.

    Baral, R., Davis, G. C. & You, W. Consumption time in household production: Implications for the goods-time elasticity of substitution. Econ. Lett. 112, 138–140 (2011).

    Article  Google Scholar 

  40. 40.

    Brenčič, V. & Young, D. Time-saving innovations, time allocation, and energy use: Evidence from Canadian households. Ecol. Econ. 68, 2859–2867 (2009).

    Article  Google Scholar 

  41. 41.

    Hamermesh, D. S. Time to eat: household production under increasing income inequality. Am. J. Agric. Econ. 89, 852–863 (2007).

    Article  Google Scholar 

  42. 42.

    Jalas, M. A time use perspective on the materials intensity of consumption. Ecol. Econ. 41, 109–123 (2002).

    Article  Google Scholar 

  43. 43.

    Cogoy, M. Dematerialisation, time allocation, and the service economy. Struct. Change Econ. Dynam. 15, 165–181 (2004).

    Article  Google Scholar 

  44. 44.

    Gomi, K., Shimada, K. & Matsuoka, Y. A low-carbon scenario creation method for a local-scale economy and its application in Kyoto city. Energy Policy 38, 4783–4796 (2010).

    Article  Google Scholar 

  45. 45.

    Shimada, K., Tanaka, Y., Gomi, K. & Matsuoka, Y. Developing a long-term local society design methodology towards a low-carbon economy: An application to Shiga Prefecture in Japan. Energy Policy 35, 4688–4703 (2007).

    Article  Google Scholar 

  46. 46.

    Liu, Z. et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524, 335–338 (2015).

    Article  Google Scholar 

  47. 47.

    National Bureau of Statistics China China Energy Statistical Yearbook 2010 (China Statistics Press, Beijing, 2010).

  48. 48.

    Sichuan 12th Five-Year Population Development Plan (The People’s Government of Sichuan Province, 2012);

  49. 49.

    Zeng, Y., Land, K. C., Gu, D. & Wang, Z. Household and Living Arrangement Projections: The Extended Cohort-Component Method and Applications to the US and China (Springer, 2013).

  50. 50.

    China to Protect Migrant Workers’ ‘Left-Behind’ Children BBC News Asia (15 February 2016);

  51. 51.

    Fridley D. et al. China Energy and Emissions Paths to 2030 Report No. LBNL-4866E (Ernest Orlando Lawrence Berkeley National Laboratory, 2012);

  52. 52.

    China Electric Power Industry Satistics Analysis 2011 (China Electricity Council, 2011)

  53. 53.

    Zhang, L., Li, H. & Gudmundsson, O. Comparison of district heating systems used in China and Denmark. Euroheat Power (Engl. edn) 10, 12–19 (2013).

    Google Scholar 

  54. 54.

    Dai, Y. & Hu, X. Potential and Cost Study on China’s Carbon Mitigation Technologies (China Environment Press, Beijing, 2013).

  55. 55.

    Kainuma, M., Matsuoka, Y. & Morita, T. Climate Policy Assessment: Asia-Pacific Integrated Modeling (Springer Japan, 2011).

  56. 56.

    SAC/TC20, CECA, CSP. Standards Collection of Energy Consumption per Unit Product (China Standard Press, Beijing, 2014).

    Google Scholar 

  57. 57.

    National Bureau of Statistics China China Statistical Yearbook 2009 (China Statistics Press, Beijing, 2009).

Download references


This work was supported by the programme ‘Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship for Foreign Researchers’. The authors acknowledge financial support received through China’s National Key R&D Program (2016YFA0602603), and the National Natural Science Foundation of China (no. 71603020, no. 71521002 and no. 71642004). We also acknowledge the support of the National Bureau of Statistics China in sharing the 2008 time-use data with us, and acknowledge the support of the Joint Development Program of Beijing Municipal Commission of Education. We thank B. van Ruijven for his kind help in providing information for the Population–Environment–Technology (PET) model.

Author information




B.Y. designed the research and performed the analysis. Y.M. conceived the paper. Y.-M.W. and G.K. contributed to the methodology improvement and scenario design. All authors contributed to writing the paper.

Corresponding authors

Correspondence to Biying Yu or Yi-Ming Wei.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Supplementary Information

Supplementary Tables 1–5, Supplementary Figures 1–5, Supplementary Note 1 and Supplementary References

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yu, B., Wei, YM., Kei, G. et al. Future scenarios for energy consumption and carbon emissions due to demographic transitions in Chinese households. Nat Energy 3, 109–118 (2018).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing