Characteristics, Decoupling Effect And Driving Factors of Regional Tourism's Carbon Emissions In China

Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data suggest weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect on emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.

Multi-Perspective Analysis of Household Carbon Dioxide Emissions from Direct Energy Consumption by the Methods of Logarithmic Mean Divisia Index and σ Convergence in Central China

Currently, household carbon dioxide (CO2) emissions (HCEs) as one of the leading sources of greenhouse gas (GHG) have drawn notable scholarly concern. Thus, here, taking six provinces in the period of 2000–2017 of Central China as a case, we analyzed the characteristics and the driving factors of HCEs from direct energy consumption and three perspectives: Central China as a whole, urban-rural differences, and inter-provincial comparison. The drivers of direct HCEs were analyzed by the Logarithmic Mean Divisia Index (LMDI). The σ convergence was adopted for analyzing the trend of inter-provincial differences on the HCEs. The key findings are as follows. First, all the direct HCEs from three perspectives had an obvious growth trend. The total direct HCEs grew from 9596.20 × 104 tonnes in 2000 to 30,318.35 × 104 tonnes in 2017, with an increase of 2.16 times. Electricity and coal use were the primary sources. The urban and rural increases of direct HCEs were up 2.57 times and 1.77 times, respectively. The urban-rural gap of direct HCEs narrowed first and then widened. The direct HCEs in the six provinces varied significantly, but the gap was narrowing. Second, as a whole the per capita consumption expenditure and energy demand were the main drivers to the increment of HCEs, with cumulative contribution rates of 118.19% and 59.90%. The energy price effect was mainly responsible for the mitigation of HCEs. Third, the similar drivers’ trend can also be seen from the perspective of inter-provincial comparison. However, from the perspective of urban and rural difference, the population urban-rural structure effect played a reverse influence on both urban and rural areas. Thus, raising the energy prices appropriately, upgrading the residents’ consumption to a sustainable pattern, controlling the growth of population size reasonably, and optimizing the household energy structure might effectively mitigate the growth of HCEs in Central China.

Decomposing drivers of changes in productive and domestic water use based on the logarithmic mean Divisia index method: a regional comparison in Northern China

It is crucial to consider regional heterogeneity while analyzing drivers of changes in sectoral water use for developing differentiated and effective demand-regulation strategies in China. By using the logarithmic mean Divisia index method, this study compares dynamic influences of intensity, structure and scale factors on changes in productive and domestic water use during 2003–2017 between Tianjin (a socio-economic developed region) and Hebei (less-developed). The results show that the scale effect stimulated the growth of productive water use in both regions, while structure and intensity effects restrained such growth. The three effects all stimulated the growth of domestic water use in most years in both regions. In both regions, the largest contributor to changes in productive and domestic water use was the scale and intensity effect, respectively. However, in the two regions, the synergies of three effects resulted in different change trends of productive water use, and cumulative contributions of sub-sectors to the intensity, structure and scale effects were not exactly the same. Tianjin and Hebei need to keep on adjusting industrial structure and lowering water-use intensity to control future growth of productive water use and take strict measures to tackle the increasing trend of domestic water use but should have different policy implementation focuses.