Heuristics for a simple and transparent Consequential LCA

Consequential Lifecycle assessment is concerned with predicting the environmental impacts of adopting a new technology taking into account how markets will adjust to the technology shock (Rajagopal 2014). Since market... [ view full abstract ]

Consequential Lifecycle assessment is concerned with predicting the environmental impacts of adopting a new technology taking into account how markets will adjust to the technology shock (Rajagopal 2014). Since market adjustment depends on the policy regime supporting the diffusion of the technology, such as whether it is aided by a mandate or a subsidy, a CLCA is also policy-specific. CLCA, therefore, requires an economic model containing the markets for the goods that are affected by the technology or policy in question (Rajagopal and Zilberman 2013). This statement raises two further questions. One is that what is meant by an economic model for there are several to chose from. Partial equilibrium (PE) models, computable general equilibrium models (CGE), and econometric models, are examples of a few different types of economic models that are used for different purposes and each one can vary in the level of detail and complexity. The second question is how does one determine which are the commodities whose markets are to be included in a CLCA. This paper is on the second question. There does not exist any obvious criteria for selecting or leaving out specific commodities while including each and every distinct commodity is unrealistic, and possibly even unnecessary. To this end, this paper presents “heuristic guidelines” that could be identify vulnerable points in the economy from which large unintended additional pollution might manifest. The heuristics are intended as a guide to identify the processes or activities that may give rise to additional emissions beyond the supply chain. The need for a heuristic approach that relies on simple rules is motivated by the complexity, cost, and the uncertainty in estimates associated with techniques and tools currently being relied upon by decision makers for predicting the global and long-term consequences from the diffusion of emerging technologies. The so called indirect land use change effect of biofuel is a case in point. Where as a comparison of the production of ethanol (from existing farmland) and gasoline would suggest that ethanol has lower greenhouse gas intensity relative to gasoline as a transportation fuel, the large scale production of ethanol has induced an expansion of farmland. The heuristics derived will be applied to technologies such as biofuels, electric vehicles and energy-efficiency improvements. The heuristics will be derived based on the following characteristics of each economic sector associated directly with the supply chain of the technology in question and the technologies and process it aims to substitutes. Examples of what these characteristics might be include i) The price elastic supply of and demand for the output of a sector?; ii) The fraction of overall market that will be affect by the new technology in question?; iii) The difference in the emission intensity of the marginal supply and the current average emissions intensity in any given sector.