1 Intelligent Well Technology: Status and Opportunities for Developing Marginal Reserves SPE
Is China’s productivity growth a mixed blessing in curbing energy intensity?
Baiding Hu[1]
Department of Accounting, Economics and Finance, Faculty of Commerce
Lincoln University, Christchurch, New Zealand
Overview
To date, the majority of the research on China’s energy intensity has focused on the measurementand decomposition of it. While energy intensity decomposition sheds light on the causes of energy intensity movement, it often does so at a relatively aggregate level. As a result, the energy intensities at a disaggregate level were almost inevitably treated exogenous and therefore used to explain the higher level energy intensity. The present paper addresses this issue by investigating the causes of energy intensity movement at the subsector level within China’s manufacturing sector. In particular, the subsector energy intensity was modelled as a function of productivity growth and energy prices. The productivity growth was estimated with subsector input-output relations taken into account. It was found that for 6 of the 12 subsectors, not only has productivity growth helped curbing energy intensity, but also has accounted for more reduction in energy intensity than have energy prices. However, the opposite was found in 4 subsectors with the remaining 2 subsectors experiencing neither. The study also provided a characterisation of the subsectors to visualise their differences in energy-utilisation technology.
Methods
Input-output tables, seemingly unrelated regression estimator, generalised method of moments estimator.
The input-output tables provide subsector-specific cost shares of the fuels, which can be used to construct a measure of subsector similarity of fuel consumption as the follows.
Denote the cost shares of the five fuels for sector i by, then the Euclidean distances between and is,= . A graphical summary of subsector similarity based on the measure is provided below in the Results section. On the graph where “1” represents the energy production subsector, it is clear that the energy production subsector is dissimilar to the rest of the subsectors.
Subsector i’s energy intensity at time t, , is modelled as a function of the subsector’s TFP growth rate, , and energy price , which is the energy price index at the economy level since such data at the subsector level are not available. The energy price index variable is included to control for price induced energy intensity changes at the subsector level.
The model for the subsector energy intensity is as follows,
(1)
where is a dummy variable equal to 1 if the i-th subsector is Energy Production and 0 otherwise, and is a disturbance term reflecting shocks to the energy intensity. Estimation results are presented in the table below in the Results section.
Results
19871992
19972002
Fig.2. Subsector (dis)similarity as measured by their distances in energy-utilisation technology
Table 7
Estimation results for subsector energy intensity models
Eq. (1) / Eq. (4) / Eq. (5)/ 1.292 / / 2.026 / / 1.310
/ -0.183 / / 2.071 / / -0.013
/ -0.502ǂ / / 0.866 / / 0.402
/ -0.258 / / 0.768 / / -0.397
/ 0.014 / / 1.458
/ -0.064ǂ / / 2.184
/ -1.426 / / 2.463
/ 0.024 / / 2.062
/ 0.373 / / 0.771
/ -0.789 / / 0.508
/ -0.596 / / 0.369
/ -0.522 / / 0.063
/ 0.202 / / -0.022
/ 0.398 / / 0.139
/ -0.403 / / -0.372
ǂinsignificant even at 10%.
Conclusions
Research on productivity in the context of China has been mainly concerned with measuring productivity growth, its determinants and its role in keeping the economy buoyant. Issuesrelating to how productivity growth impacts on energy demand in China have not received sufficient attention. The present research addresses an aspect of the issues by investigating how TFP growth impacts on energy intensity at the subsector level within the manufacturing sector. This research was also prompted by the fact that the decomposition analysis of China’s energy intensity tends to leave subsector energy intensity unexplained.
The structural information contained in the 1987, 1992, 1997 and 2002 Chinese input-output tables was exploited to characterise subsector (dis)similarity in energy consumption and to assist TFP growth estimation. It was found that the energy production subsector was very dissimilar to the rest of the subsectors in terms of energy-utilisation technology. This information was taken up when specifying the functional form of the subsector energy intensity model in that the difference between the energy-producing and the rest of the subsectors in the response to an energy price shock was treated separately, and the significance of such a difference was supported by the data.
It was found that AEEI was significant in six of the subsectors, while it seemed absent in two subsectors, namely, Mining and Timber, Paper and Printing as the statistical evidence was insignificant. Two of the four subsectors for which the absence of AEEI was significantly supported by the data are Non-metallic Products and Metal Products, both are heavy energy users. Given that AEEI is largely driven by technological innovations, it seems reasonable that not all subsectors had experienced a non-price induced energy-saving technical progress. Consistent with the literature, the role of energy price was confirmed in dampening the rise of energy intensity. However, in the subsectors where there was a strong presence of AEEI, it exerted less influence than AEEI in curbing the subsector energy intensity.
A major limitation of this study stems from the inability to ascertain whether the subsector TFP growth was completely due to non-price-induced technical progress. If not, then the validity of the energy intensity model rests on the assumption that the price-induced and the non-price-induced proportions grew at a more or less equal pace. It would also be much nicer if the differences in the subsector distances were statistically testable. Due to the relatively small sample size, the variables were assumed stationary.
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