With the implementation of the ＂Development of Western China＂ strategy, this region has become the fastest growing economic area in China. However, rapid economic growth has resulted in a substantial increase in carbon emissions and affected energy reduction goals. In order to effectively control the rapid increase in carbon emissions across western China, we need a comprehensively analyze the main factors causing these increases. Here, we analyze the relationship between economic development patterns and carbon emissions. The findings suggest that consumption upgrades and industrial transformation have a positive correlation with carbon emissions in this region. We then conducted an econometric FGLS analysis on the relationship and its transmission mechanism between economic growth and CO2 emissions with cross-province panel data from 1991 to 2009. A positive correlation was found, and the relationship is more significant after the implementation of the western development strategy. The influence coefficient of change in primary, secondary and tertiary industries is 16.4. The influence coefficient of increased share of heavy industry and extractive industry in the secondary industry is 14.3, and the influence coefficients of per-capita living expenditure and per capita traffic expenditure are 5.6 and 6.5. Traditional population size and income scale have a weak impact on carbon emissions, and the influence coefficients of population size and income scale are only 0.73 and 0.86. GDP increases have a second major impact on the carbon emissions. Energy intensity has a negative relationship with carbon emissions and urbanization level has a positive relationship （coefficients are -8.2 and 4.65）.
Here, the geographical space distribution of the oil and gas industry in China is comprehensively investigated using the overal Moran’s I index and local Moran’s I index. We found that China’s oil and gas industry development from 2000 to 2010 has a differentiated geographical space distribution upstream （extractive industry） but not downstream （reifning industry）. To analyze upstream and downstream states a spatial econometrics model （SEM） was used to identify inlfuential factors resulting from the spatial concentration of the oil and gas industry. An external effect is the key factor promoting the spatial concentration of the upstream industry in China;governmental economic policy is another important factor.
