2 Research on Urbanization and Low-Carbon Development in China
61
small cities and towns and ease their geographical weakness. Ensure coordinated
development of large, medium and small cities and towns, which could co-build
and share infrastructures, and benefit from the transfer of functions and industrial capacity from megacities, creating mutual benefits and win-win outcome.
Smat cities can enable a robust response system for smart public service and
urban management. Aided by information technology, professional application
systems could be strengthened, including employment, health care, culture, and
housing among others, to promote urban data integration, cross-departmental
information sharing, interactions among industries and accurate management
of urban operation, etc. Through activating dynamic city-wide response to incidents and various disasters, future cities would be better positioned to prevent,
prepare for, respond to, and recover from disasters – the testament to meaningful
and stronger smart cities.
References
Dietz, T., & Rosa, E.A. (1997). Effects of population and affluence on CO 2 emissions. Proceedings
of the National Academy of Sciences of the United States of America.
Ehrlich, P. R., & Holdren, J. P. (1971). Impact of population growth. Science, 171(3977), 1212–1217.
Emrah, K., & Ulucak, Z. ¸
S. (2019). The effect of energy R&D expenditures on CO 2 emission reduction: Estimation of the stirpat model for OECD countries. Environmental Science and Pollution
Research.
Houkai, W. (2014). Polarization tendency and scale pattern reconstruction in China’s urbanization
process. China Industrial Economy, 03, 18–30.
Jiagui, C. (2008). Report on China’s Industrialization Process. Social Sciences Academic Press.
Jianjun, W., & Zhiqiang, W. (2009). Division of urbanization development stages. Chinese Journal
of Geography, 64(02), 177–188.
Jiankun, H. (2013). CO 2 emission peak analysis: China’s emission reduction targets and solutions.
China Population, Resources and Environment, 12, 1–9.
Li, P., & Cai, F. (2015). 2020 Toward a moderately prosperous society in all respects. Social Sciences
Academic Press.
Limei, M., Dan, S., & Qingbing, P. (2018). China’s low-carbon energy transition (2015–
2050): Renewable energy development and its feasible path. China Population, Resources and
Environment, 02, 8–18.
Wang, Y., & Liancheng, Z. (2015). Research on influencing factors and mechanism of the transformation of China’s manufacturing industry towards low-carbon economic growth—analysis
of dynamic panel data of 28 manufacturing industries based on STIRPAT model. Economics
Dynamics, 000(004), 35–41.
Weidou, N., Yong, J., Linwei, M., & Shanying, H. (2015). Preliminary discussion on the strategy of
China’s energy consumption revolution and total energy consumption control. China Engineering
Science, 17(09), 111–117.
York, R., Rosa, E. A., & Dietz, T. (2003). Stirpat, ipat and impact: Analytic tools for unpacking the
driving forces of environmental impacts. Ecological Economics, 46(3), 351–365.
61
small cities and towns and ease their geographical weakness. Ensure coordinated
development of large, medium and small cities and towns, which could co-build
and share infrastructures, and benefit from the transfer of functions and industrial capacity from megacities, creating mutual benefits and win-win outcome.
Smat cities can enable a robust response system for smart public service and
urban management. Aided by information technology, professional application
systems could be strengthened, including employment, health care, culture, and
housing among others, to promote urban data integration, cross-departmental
information sharing, interactions among industries and accurate management
of urban operation, etc. Through activating dynamic city-wide response to incidents and various disasters, future cities would be better positioned to prevent,
prepare for, respond to, and recover from disasters – the testament to meaningful
and stronger smart cities.
References
Dietz, T., & Rosa, E.A. (1997). Effects of population and affluence on CO 2 emissions. Proceedings
of the National Academy of Sciences of the United States of America.
Ehrlich, P. R., & Holdren, J. P. (1971). Impact of population growth. Science, 171(3977), 1212–1217.
Emrah, K., & Ulucak, Z. ¸
S. (2019). The effect of energy R&D expenditures on CO 2 emission reduction: Estimation of the stirpat model for OECD countries. Environmental Science and Pollution
Research.
Houkai, W. (2014). Polarization tendency and scale pattern reconstruction in China’s urbanization
process. China Industrial Economy, 03, 18–30.
Jiagui, C. (2008). Report on China’s Industrialization Process. Social Sciences Academic Press.
Jianjun, W., & Zhiqiang, W. (2009). Division of urbanization development stages. Chinese Journal
of Geography, 64(02), 177–188.
Jiankun, H. (2013). CO 2 emission peak analysis: China’s emission reduction targets and solutions.
China Population, Resources and Environment, 12, 1–9.
Li, P., & Cai, F. (2015). 2020 Toward a moderately prosperous society in all respects. Social Sciences
Academic Press.
Limei, M., Dan, S., & Qingbing, P. (2018). China’s low-carbon energy transition (2015–
2050): Renewable energy development and its feasible path. China Population, Resources and
Environment, 02, 8–18.
Wang, Y., & Liancheng, Z. (2015). Research on influencing factors and mechanism of the transformation of China’s manufacturing industry towards low-carbon economic growth—analysis
of dynamic panel data of 28 manufacturing industries based on STIRPAT model. Economics
Dynamics, 000(004), 35–41.
Weidou, N., Yong, J., Linwei, M., & Shanying, H. (2015). Preliminary discussion on the strategy of
China’s energy consumption revolution and total energy consumption control. China Engineering
Science, 17(09), 111–117.
York, R., Rosa, E. A., & Dietz, T. (2003). Stirpat, ipat and impact: Analytic tools for unpacking the
driving forces of environmental impacts. Ecological Economics, 46(3), 351–365.