Residential energy consumption (REC) is now the second largest energy sector in China. However, decoupling analysis of REC has not received enough attention. Here, we explore the decoupling relationship between REC and economic growth in urban and rural Guangdong from 2000 to 2017. First, we use the Tapio decoupling model to study the decoupling state. Then, key drivers affecting the decoupling were explored by combining the Tapio decoupling model with the Log-mean Divisa Index (LMDI) method. Finally, the decoupling efforts were evaluated by using the decoupling efforts model. The main results are as follows: (1) The decoupling state in urban regions was better than that in rural regions. Urban regions experienced weak decoupling (WD) in most years during 2000–2017, while expansive negative decoupling (END) mostly was seen in rural regions. (2) The per capita income effect was the most important driver inhibiting decoupling in Guangdong urban and rural regions. The energy intensity effect was the biggest driver promoting decoupling in urban Guangdong, followed by the family size effect. In contrast, the family size effect exerted the dominant influence on accelerating the decoupling in rural Guangdong. Overall, the household effect inhibited decoupling in urban regions, but promoted decoupling in rural regions. (3) The decoupling efforts results suggest that the energy efficiency improvement played a major role in the decoupling efforts of urban Guangdong, while the decrease of family size in rural Guangdong was the leading contributor to the decoupling efforts.

The residential sector is the second-largest consumer of energy in China. However, little attention has been paid to reducing the residential CO₂ emissions of China’s less developed or undeveloped regions. Taking Jiangxi as a case study, this paper thus aims at fully analyzing the difference of the residential energy-related CO₂ emissions between urban and rural regions based on the Log-Mean Divisia Index (LMDI) and Tapio decoupling model. The main results are showed as follows: (1) Since 2008, residential energy-related CO₂ emissions have increased rapidly in both urban and rural Jiangxi. From 2000 to 2017, the residential energy-related CO₂ emissions per capita in rural regions rapidly increased and exceeded that in urban regions after 2015. Furthermore, the residential energy structures had become multiple in both urban and rural regions, but rural regions still had room to optimize its energy structure. (2) Over the study period, consumption expenditure per capita played the dominant role in increasing the residential energy-related CO₂ emissions in both urban and rural regions, followed by energy demand and energy structure. Energy price had the most important effect on decreasing the urban and rural residential energy-related CO₂ emissions, followed by the carbon emission coefficient. However, urbanization increased the urban residential energy-related CO₂ emissions but decreased the CO₂ emissions in rural regions. Population made marginal and the most stable contribution to increase the residential energy-related CO₂ emissions both in urban and rural regions. (3) Overall, the decoupling status showed the weak decoupling (0.1) and expansive negative decoupling (1.21) in urban and rural regions, respectively.

Currently, little attention was paid to the tourism’s CO₂ emission (CE) at province level. Thus, taking Hainan as a case, we computed this province’s CE, and analyzed the relationship between Hainan’s tourism economy and its CE, and the drivers of the CE. The results showed that Hainan’s tourism CE increased rapidly from 99.88×10⁴ t in 2001 to 475.07×10⁴ t in 2015. Particularly, Tourism transport always accounted for the largest proportion of tourism CE (more than 74%). Moreover, Hainan presented a holistic weak decoupling (0.68) during 2001-2015. But the decoupling rate was only 57.14%. Thus, Hainan still has much potential to improve the energy-use efficiency of tourism industry for accelerating the decoupling process. In addition, the effect of population was the dominant driver to promote Hainan’s tourism CE followed by expenditure size effect with the contribution rates of 132.52% and 11.78%, respectively. Whereas energy intensity effect played the most primary role in inhibiting CE followed by industrial structure effect, and their contribution rates were -38.65% and -5.58%, respectively. Last, based on these results above, some reasonable countermeasures and suggestions are proposed.

Using the bottom-up method and Tapio model, we calculated the tourism energy consumption (EC) and corresponding carbon emission (CE) in Hubei of China, and analyzed the decoupling between the CE and tourism economy. The results showed the tourism CE was 111.84×10⁴ t in 2001, and increased to 376.04×10⁴ t in 2015, with the annual growth amount of 18.87×10⁴t and rate of 9.05%, respectively. This denoted Hubei’s tourism was being thoroughly developed on the whole from 2001 to 2015. Then, the share of tourism transport CE was always not less than 62% during 2001-2015, which indicated tourism transport sector should be paid more attention to and be given more of corresponding mitigation countermeasures. Furthermore, tourism activities’ CE was always becoming larger and larger, with the increases of people’s living standard and leisure time. Thus, it was necessary to study on how to improve the efficiency of energy use, especially in the progress of tourism activities. Last, an overall weak decoupling relationship between tourism economy and the corresponding CE existed within Hubei in 2001-2015. Thus, the economic development mode of Hubei’s tourism industry was transforming to the low-carbon pattern expected by us, but it still needed to be continually strengthened.