Potentials and Challenges for Using the Clean Development Mechanism for Transport-Efficient Development : A Case Study of Nanchang , China

We propose a methodology, consistent with the Kyoto Protocol’s Clean Development Mechanism (CDM), to quantify the greenhouse gas (GHG) emission reduction benefits of transport efficient development (TED). The TED concept aims to reduce transportation GHGs via interventions in urban development patterns. We use the Nanchang Transit-Oriented Development project in China to demonstrate the methodological approach and, thus, the possibilities for bringing such projects into the carbon market. The case illustrates the opportunities for and challenges to using the CDM to reduce transportation GHG emissions by changing urban development patterns. While theoretically promising, utilizing the CDM for TED involves important methodological challenges. The proposed analytical approach, utilizing a “control group,” faces difficulties regarding geographical control, the reliability of the statistical techniques, and challenges to calculating emissions “leakage.” These methodological difficulties also impact financial viability because of high monitoring costs and high project risks. INTRODUCTION Transportation currently accounts for one-quarter of the world’s energy-related carbon dioxide (CO2) emissions and is expected to be the most rapidly growing source over the next 30 years, increasing at an annual rate of 2% to 3% (1). The developing world will account for the largest share of this growth, with forecasted growth rates between 3.5% and 5.3% per year, as compared to 1.2% to 1.4% in the OECD. Given these forecasts, the developing world will shift from accounting for roughly 35% of world transportation greenhouse gas (GHG) emissions in 2000 to 52% to 63% by the year 2030 (1). To modify these emission growth trajectories, we will likely need a suite of technology, policy and pricing approaches, focusing on both passenger and freight transportation at both the urban and interurban levels. In the shortto medium-term, technological fixes alone will most likely not provide the hoped-for “silver bullet.” Heywood et al (2) provide a sense of the challenges; their assessment of plausible vehicle technological improvements in the U.S. private passenger vehicle market leads them to the “sobering overall conclusion” that technology and demand management options – i.e., behaviorallybased interventions – together will be required. In this context, we cannot ignore passenger transport: personal mobility consumes roughly twothirds of transportation energy today, a share expected to remain fairly stable over the next 50 years (3). Nor can we ignore the developing world’s metropolitan areas, where population will double by 2030, representing 95% of net global population growth, or 1.94 billion additional people (4). Changing passenger travel behavior in urban areas of the developing world may be crucial to reducing long run transport GHG emissions. Perhaps no developing country better represents the challenges and opportunities for reducing urban transportation GHG emissions than China. The country is undergoing a major demographic transition of rapid and intense urbanization, coupled with sustained economic growth, and changes in consumer behavior and industrial and economic activity. While China already has more than 100 cities with 1 million or more persons, just 40% of its total population currently lives in urban areas – this share will increase to 60% by 2030 (4). At the same time, motorization continues apace; the country is already the world’s fourth largest automobile producer and the third largest consumer. In this decade, car sales have been growing by 70% per year. These forces – combined with economic reforms, fiscal decentralization, and changing consumer expectations with respect to, e.g., residential space – are dramatically transforming China’s urban landscape. For example, during the 1990s Shanghai’s population density declined by 50%. While necessary as cities modernize and business and residential space demands increase, these transformations raise the question: how can we capitalize on the dynamism of urban China to create more energy efficient cities? Specifically, to what degree can alternative future urban development patterns change travel behavior to help move China towards a less energy-intensive urban transportation future? Can the current carbon market play a meaningful role in inducing such changes? To help answer these questions, we Zegras, Chen, Grütter 2 outline a methodology for quantifying the transportation GHG reduction benefits of an urban land development project, using a specific project in Nanchang China for illustration. THE POTENTIAL ROLE OF THE BUILT ENVIRONMENT Interest in modifying urban development patterns to influence transportation energy consumption, emissions, and travel behavior has a long history. We can find urban simulation models of “hypothetical cities” in the 1960s (e.g., 5); numerous more recent regional travel modeling applications to actual cities (e.g., 6); empirical analyses attempting to quantify the built environment’s influence on travel behavior (e.g., 7); and policies and projects that have taken various approaches to try to leverage the built environment-travel behavior relationship (e.g., 8, 9). Schipper et al’s “ASIF” framework (10) provides a way to understand the components contributing to transportation energy use and emissions. The ASIF optic reveals the essential determinants of transportation energy use: total activity (A), mode share (S), fuel intensity (I), and fuel type (F) (thus, ASIF) (see Figure 1). Land development patterns potentially influence three of the four components (activities, mode share, and fuel intensity), since urban size, form, density, land use mix, and design (including street patterns) may affect the distribution of activities and total travel distances (e.g.,11), mode choice (e.g.,12), and vehicle occupancy (e.g.,13). FIGURE 1 ASIF: Determinants of Urban Passenger Transportation Energy Use/GHG Emissions (14 ) Note: pkt = passenger kilometers traveled. In the following methodology, we propose the generic concept of transport efficient development (TED), which encompasses a range of investments and policies which attempt to improve transport efficiency through land development. The TED idea builds upon the ideas popularized under the term Transit Oriented Development (TOD) – integrating land development with public transportation by concentrating dense, mixed-use, and pedestrian-friendly urban “nodes” around public transportation stations. Extending on TOD, TED includes: • A transit-oriented development density gradient – increasing the number of persons and activities close to transit stations, the attractiveness of public transit relative to driving, and transit ridership and efficiency; • Mixed land use – decreasing distances between destinations thereby shortening average travel distance and making non-motorized transport (NMT) more attractive; • Dense and well-connected road/transit network – reducing travel distances and increasing the attractiveness of NMT and public transport; • Pedestrianand NMT-friendly facility and urban design – making NMT more attractive; Zegras, Chen, Grütter 3 • Self-sustained community services – reducing travel distance for certain trip purposes, reducing vehicle distances traveled, and increasing attractiveness of NMT; • Car use restrictions, efficient parking management, traffic calming, etc. – making driving relatively less attractive, thereby inducing shifts to less GHG-intensive modes. Despite its intuitive appeal, using TED to modify travel behavior faces numerous institutional, financial and other challenges. In addition, when aiming to understand the potential travel behavior effects of land development interventions, we face a number of analytical challenges, including lack of adequate data, modeling complexity, etc. Analytical Challenges One important theoretical and empirical challenge in understanding the built environment’s role in transport GHGs relates to the issue generally referred to as “self-selection” (see (15) for a good review of the issues and relevant analytical approaches). In this specific context, “self-selection” arises because, in using comparative analyses of individuals’ behavior (e.g., residents in a TED versus residents in a nonTED) to determine the built environment’s influence on travel, we may incorrectly attribute the outcome (behavior) to the supposed cause (built environment). By using observed outcomes and characteristics, our attempt to infer causality from the built environment to travel behavior may be confounded by other unobserved characteristics – such as individuals’ attitudes towards or preferences – which may be responsible for at least part of the travel outcome. In the TED case, for example, some residents living in the project area may have chosen to move there because the TED allows them to more easily travel how they would have traveled anyway (e.g., by certain modes). Considerable research in recent years has attempted to control for, and understand the impacts of, self-selection in the built environment-travel behavior realm (as summarized in 15). Most studies do detect self-selection, although few explicitly quantify the relative influence. A recent analysis of residential choice, vehicle ownership choice, and walking levels in New York City estimates that self-selection accounts for one-third to one-half of the estimated influence of the built environment (measured as population density) on walking levels (16). Note that self-selection presents a challenge in comparative analyses (e.g., TED versus non-TED) as well as in simulations/forecasts of future conditions since parameters in the latter should be based on observed behaviors which might be biased from self-selection. We cannot ignore this issue when attempting to finance TED projects via the carbon market, for which emissions must be quantifiable, verifiable, and additional to business as us