سیاست زیست محیطی، تصمیم گیری و اثرات بازگشت در حمل و نقل

ABSTRACT Environmental policy, decision making, and rebound effects in the U.S. trucking sector

Heavy-duty vehicles (HDVs), including large trucks and vocational vehicles such as buses, dump trucks and utility vehicles, consume a significant and increasing share of fuel in the US. For example, HDV Class 7–81 energy consumption was ~17.6% of total transportation energy use in 2013,2 and is projected to reach almost 30% of transportation energy use by 2040 (U.S. Energy Information Administration, 2016; Oak Ridge National Laboratory, 2015). This increasing use of energy in the trucking sector has obvious impacts on emissions of greenhouse gases (GHG) and criteria pollutants, and the US government has been active in promoting technologies and policies that would promote more efficient vehicle operations (EPA & NHTSA, 2011). In particular, the U.S. Environmental Protection Agency (EPA) and the National Highway Transportation and Safety Administration (NHTSA) have promulgated regulations aimed at improving HDV fuel efficiency. EPA now regulates GHG emissions from trucks (e.g., with performance standards for gCO2/ ton-mile), and NHTSA regulates fuel consumption (in gallons/1000 tonmile). These regulations are being implemented in stages, with Phase I standards affecting trucks produced between model years 2014 and 2018 (The White House, 2014a) and Phase II standards affecting trucks produced between model years 2019 and 2025 (The White House, 2014b).

The ability of these new standards to meet energy and GHG reduction goals is dependent not only on the stringency of the standards themselves, but also on the response to these standards by firms operating in the trucking sector. Researchers are now trying to understand how decision-making by trucking firms are influenced by these standards, which have the interesting impact of possibly lowering the operating costs of trucking firms. Of particular interest is the extent of the rebound effect, which has been studied extensively in the light-duty vehicle (LDV) sector, but is a nascent area of research for HDVs (De Borger & Mulalic, 2012; Matos & Silva, 2011; Winebrake et al., 2012; Berkhout, Muskens, & Velthuijsen, 2000; Greene, 2012; Greene, Kahn, & Gibson, 1999; Greening, Greene, & Difiglio, 2000; Small & Van Dender, 2005; Sorrell & Dimitropoulos, 2007; Winebrake et al., 2015a, b). A positive rebound effect suggests that as vehicles become more efficient and their operational costs per mile decrease, firms will increase fuel consumption either directly (through additional travel or behavioral modifications) or indirectly (by passing along fuel savings to customers and inducing greater demand for HDV services) (Winebrake et al., 2012).

Empirical research to quantify the rebound effect for the HDV sector has shown mixed results, with some studies pointing to high rebound effects and others implying negligible effects in both the US (Winebrake et al., 2015a, b; Leard et al., 2015) and in other countries (De Borger & Mulalic, 2012; Matos & Silva, 2011; Wadud, 2016; Wang & Lu, 2014). The variety of results is due to several factors, including the geographical elements of each study; the heterogeneity of the sector; the application of different statistical models; the difficulty in separating direct and indirect effects; and the lack of detailed, disaggregated data on HDV travel behavior, to name a few.

This paper takes a qualitative approach to understanding the basis of the rebound effect in the US trucking sector by considering more generally firm decision making in light of changing policy and fuel price landscapes. In addition to presenting a review of the literature on this topic, we have conducted eight (8) in-depth interviews with trucking fleets to explore how they make decisions with respect to: (1) responses due to fuel price changes or energy efficiency improvements in their fleet; (2) the pass-through of fuel cost savings to customers or others (e.g., shareholders); (3) the management of vehicles; and (4) the management of driver behavior. We also explore their responses in the context of their key objectives (e.g., profit maximization; service quality; safety; etc.) and competitive advantage to better understand how incentives/disincentives might affect firm behavior, recognizing that market share is not based on costs alone. Note that for certain firms or services, there is no need for increased service by customers (i.e. waste management or other services). Our goals are to use these interviews to gain greater understanding of decision making in the trucking sector to validate existing theory or quantitative findings, suggest new theoretical models for future empirical testing, or identify where additional research is needed. Although our focus is on the US trucking sector, we believe many of our results may also be applied in other international contexts.

The next section provides additional background information related to the rebound effect in the HDV sector, followed by a discussion of our methods and results. The final section places our results in the context of new policies and proposed research necessary to better understand and quantify the rebound effect in the trucking sector.