Gasoline consumption efficiency: An equivalence estimate from energy demand function and production function

Transcription

1 Gasoline consumption efficiency: An equivalence estimate from energy demand function and production function David C. Broadstock Xiaoqi Chen June 2017, Singapore

2 BACKGROUND There are two fundamental approaches to obtaining measures of energy efficiency. One can choose to take either the traditional approach embedded in the context of classical production theory, or instead follow more recently devised methods grounded in demand theory (Filippini and Hunt 2011). Given the absence of academic literature for the comparison of production function and consumer function, we analyze the theories and discuss the connections between these two different ways on measure gasoline consumption efficiency.

3 MOTIVATIONS In 2015, President Xi in China put forwarded "Supply Side Structural Reform" and "Demand Expansion". As policy maker, they want to know what is the most efficiency way to implement the reform policy: demand side or production side. If our results hold, it would suggest that in certain cases we can use efficiency estimates from a demand function as a proxy for production related efficiency and vice versa. Energy demand function is derived form production function. We can regard energy as a final product and service.

4 PRESENTATION OUTLINE A presentation in two or three parts, depending on your preferences Specify and estimate a Frontier demand function Specify and estimate an inverted production function (energy oriented sub-vector input distance function) Compare estimates of gasoline efficiency obtained using these two approaches This stage of comparison is somehow optional to the reader - it is a simple interpretive exercise, and the estimates of a gasoline demand function and a general production function hold value even without these comparisons We note that: The demand function provides a consumer theory motivated measure of efficiency Conversely the production function adopts a producer-theory based view of efficiency

5 DATA CONSIDERATIONS There are a number of data sources that can be consulted, although a single consistent source is not a luxury that should be taken for granted. Main variable is gasoline consumption per capita Data cover the years We exclude Tibet China Energy Statistical Yearbook National Bureau of Statistics of People s Republic of China Bloomberg database (HDD, CDD)

6 SOME KEY STATISTICS AND TRENDS An initial glance at the trend of gasoline Consumption of Gasoline Consumption of Gasoline per capital Beijing Shanghai Beijing Shanghai

7 ECONOMETRIC DEMAND FUNCTION SET-UP SFA model g it = a + a p p it + a y y it + a hdd hdd it + a cdd cdd it + a t t + a t2 t 2 + v it + u it For the frontier demand function approach, the present study mimics closely the empirical specification of Filippini and Zhang (2011). Price (based on 1995) Income (natural logarithm of of GDP) weather proxies UEDT ( Underlying Energy Demand Trends) v it (the normal noise term assumed to be normally distributed.) u it (contains information on the distance between the frontier and the actual input and is interpreted as an indicator of the inefficiency levels.

8 DEMAND FUNCTION ESTIMATION RESULTS Income elasticitiy a-priori Price elasticitiy a-priori Dummies for coastal and city ( Coastal:Tianjin Liaoning Hebei Shandong Jiangsu Zhejiang Shanghai Fujian Guangdong Guangxi Hainan City: Beijing Chongqing Shanghai Tianjin ) Estimation method: SFA Model specification: (1) (2) (3) (4) Estimated coefficients Intercept Income (Y) Price of gasoline (P) Heating degree days (HDD) Cooling degree days (CDD) Time Time squared Coastal dummy City-province dummy Model diagnostics/additional information Observations Provinces Years log-likelihood values Note: These tests are comparing only models using the same estimation method.

9 THE INVERTED PRODUCTION FUNCTION SFA model -g it = β 0 +β l l it +β k k it +β yy it +β ke ke it +β ngng it +β cc it +β l,l l 2 it +β k,k k 2 it +β y,yy 2 it + β ke,ke ke 2 it +β ng,ngng 2 it +β c,cc 2 it +β l,k [l it k it ]+β l,y [l it y it ]+...+a t t +a t2 t 2 +V it +U it The invented production function approach assumes a (transcendental logarithmic) flexible functional on output, capital and labor, and additionally enters time in a quadratic form to accommodate underlying energy demand trend effects. Taking gasoline as the input of interest EF it = E F it /E it = exp( Ûit) = (x it β Ûit)/x it β Following Jondrow et al(1982), E it is the observed energy consumption and Eit F is the frontier or minimum demand of the i th province in time t. A value EF it of one indicates a province on the frontier (100 % efficient), while non-frontier provinces are characterized by a level of EF it lower than 100%.

10 THE INVERTED PRODUCTION FUNCTION It is difficult to validate these estimates directly Variables SFA (1) (2) (3) (4) Estimated coefficients (level terms) β y *** β c (squared-terms) β y β ng (cross-products) β y,c β y,k β y,cok β c,k β k,cok (other variables) β t β coastal β city

11 DO THE TWO SHOW SOME SIMILARITIES On the strength of the statistical correlations between the efficiencies The pattern of correlation between the scores is not, perfect, but is very high with a net correlation across all years well in excess of 0.9. This implies a high degree of observational equivalence between the scores from the two alternative approaches. Year 2000 Year 2014 Production efficiency Production efficiency Demand efficiency Demand efficiency

12 THE CHAMPIONS OF EFFICIENCY 2000 versus where is efficiency>90%? Demand Production Anhui Anhui Anhui Anhui Chongqing Chongqing Chongqing Guangdong Guangdong Guangdong Guangxi Hebei Henan Henan Henan Jiangsu Jiangsu Jiangsu Jiangsu Jiangxi Jiangxi Jiangxi Jiangxi Ningxia Ningxia Shandong Shandong Shandong Shandong Shanghai Shanghai Shanghai Shanghai

13 CONCLUSIONS What can we learn from this study so far? This paper compares and contrasts the efficiency scores obtained from two popular re- cent model variants. A surprising degree of observational equivalence is found, at least in the context of Chinese gasoline consumption efficiency. In light of this the study will seek to elaborate on the reasons which might imply equivalence and what implica- tions/opportunities this might create energy policy.

14 THANKS FOR LISTENING!!