Understanding China's climate change mitigation policy development: Structures, processes and outcomes

Climate change is one of the most important and urgent issues of our time. Since 2006, China has overtaken the United States as the world’s largest greenhouse gas (GHG) emitter. China’s role in an international climate change solution has gained increased attention. Although much literature has addressed the functioning, performance, and implications of existing climate change mitigation policies and actions in China, there is insufficient literature that illuminates how the national climate change mitigation policies have been formulated and shaped. This research utilizes the policy network approach to explore China’s climate change mitigation policy making by examining how a variety of government, business, and civil society actors have formed networks to address environmental contexts and influence the policy outcomes and changes. The study is qualitative in nature. Three cases are selected to illustrate structural and interactive features of the specific policy network settings in shaping different policy arrangements and influencing the outcomes in the Chinese context. The three cases include the regulatory evolution of China’s climate change policy making; the country’s involvement in the Clean Development Mechanism (CDM) activity, and China’s exploration of voluntary agreement through adopting the Top-1000 Industrial Energy Conservation Program. The historical analysis of the policy process uses both primary data from interviews and fieldwork, and secondary data from relevant literature. The study finds that the Chinese central government dominates domestic climate change policy making; however, expanded action networks that involve actors at all levels have emerged in correspondence to diverse climate mitigation policy arrangements. The improved openness and accessibility of climate change policy network have contributed to its proactive engagement in promoting mitigation outcomes. In conclusion, the research suggests that the policy network approach provides a useful tool for studying China’s climate change policy making process. The involvement of various types of state and non-state actors has shaped new relations and affected the policy outcomes and changes. In addition, through the cross-case analysis, the study challenges the “fragmented authoritarianism” model and argues that this once-influential model is not appropriate in explaining new development and changes of policy making processes in contemporary China.

Predicting hurricane evacuation decisions: When, how many, and how far

Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. ^ Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. ^ Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. ^ All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation. ^

A methodology to estimate time varying user responses to travel time and travel time reliability in a road pricing environment

Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.