Energy efficiency measurement in agriculture with imprecise energy content information

Abstract

Energy efficiency measurement is crucial when planning energy reduction policies. However, decision makers understandably will be reluctant to act in the absence of solid data and results supporting a policy position. The main objective of this paper is to propose an alternative method to measure farm energy efficiency. This method is based on the Data Envelopment Analysis (DEA) approach in a cost framework introduced by Farrell (1957) and developed by Färe et al. (1985). We decompose the energy efficiency measurement into two components, namely technical and allocative efficiencies. Here, input prices are replaced by their energy content. The energy efficiency model is used to explore the optimal input-mix that produces the current outputs at minimum energy-consumption. We show that this decomposition can help policy makers considerably to design accurate energy policies. The presence of uncertainty on data, and more particularly on energy content of inputs, leads us to recommend exploiting the methodologies proposed for calculating the bounds of efficiency measurement in order to produce more robust results. We expect to alert policy-makers in the fact that efficiency is not a fixed value and should be considered with caution. A 2007 database of French farms specialized in crops is used for empirical illustration.

Linking energy efficiency legislation and the agricultural sector in South Africa

“…However, the use of energy in this sector is inefficient since most of the motors used by farm machineries and equipment are energy inefficient compared with those in developed countries in Europe. In a study conducted by Blancard and Martin (2014) to examine energy efficiency and its components (technical and allocative efficiency in energy contents) in a deterministic case (i.e., when one knows the energy content), it was revealed that energy efficiency varies across farms in France (from 0.305 to 1). They concluded that energy efficiency improvement policies should target the most energyinefficient farms, and precise energy policies designed for such farms may involve giving them incentives to reallocate inputs in a manner that corresponds to that of energyefficient farms.…”

“…Xiaohua Xia and Jianjfeng Zhang (2010) and Zhang et al (2013) maintained that energy efficiency consists of the following components: technology efficiency, equipment efficiency, operation efficiency and performance efficiency. In a study conducted by Blancard and Martin (2014) on energy efficiency measurements in agriculture, they maintained that efficiency is not a fixed value and should be considered with caution, especially in situations of data uncertainty and availability (such as the case of South Africa). Blancard and Martin defined energy efficiency in agriculture as 'the ratio between outputs in physical units or converted to energy and inputs converted to energy'.…”

“…It is not clear whether the South African legislation that promotes energy efficiency corresponds with SADC frameworks. Blancard and Martin (2014) argued that in the agricultural sector, one can measure energy efficiency as the amount of energy required per quintal of product. It is necessary to define 'energy efficiency', as the notion of 'energy efficiency' remains contested as a consequence of its many possible multi-layered and context-dependent meanings.…”

“…A 57% carbon emission reduction is possible by 2050. Blancard and Martin (2014) used the Data Envelop Analysis (DEA) similar to the costs framework and decomposed energy efficiency measurements into technical and allocative efficiencies in energy contents of inputs in agriculture. They replaced input prices used in traditional economic efficiency measurements by their energy content and used the energy efficiency model to explore the optimal input mix that can produce current outputs at minimum energy consumption.…”

Estimating Energy Consumption of Transport Modes in China Using DEA

“…It is an effective method of assessing the relative efficiency of a set of decision-making units (DMUs), with multiple inputs and outputs, and has been widely applied in many research fields. At the macro level, DEAs have been used to evaluate the energy efficiencies of various countries and regions, such as the USA [7], Japan [8][9][10], Taiwan [11,12], Sweden [13], Spain [14], France [15], India [5,[16][17][18] and Iran [19]. In addition, a set of countries was chosen for cross-country comparisons, e.g., OECD countries [20], developing countries [21], BRICS countries [22], EU countries [23] and APEC (Asia Pacific Economic Cooperation) countries [24], and the agriculture, manufacturing, power generation, transport and service sectors were considered.…”