Using Data Science Methods to Address Challenges in Sustainable Consumption and Production at Multiple Scales
Creators: name: Zhao, Bu
Publisher: name: My University
Year: 2021
Subjects: subject: Sustainable Consumption and Production
subject: Data Science
subject: Sustainability
subject: Machine Learning
subject: Data-driven apporach
subject: Natural Resources and Environment
subject: Science

description: Sustainable consumption and production (SCP) have been extensively discussed, studied, and implemented by various stakeholders in tandem to achieve greater gains in environmental sustainability. However, SCP research and practice still face substantial challenges due to various issues related to data. This dissertation sheds light on how data science methods can help improve data availability and data resolution in SCP research and practice at multiple scales. This dissertation began by addressing the challenge of data availability through a case study in the area of life cycle assessment (LCA). I developed a supervised learning model to estimate missing unit process data solely relying on existing data. The model built upon ecoinvent (version 3.1) can successfully classify the zero and non-zero flows with a low misclassification rate (0.79% when 10% of the data were missing). For non-zero flows, the model can estimate their values with an R2 over 0.7 when less than 20% of data were missing in one unit process. This model can provide critical data to complement primary data for LCA studies and demonstrates the promising applications of machine learning methods. Next, I further addressed the data availability issue often faced by SCP research at the sector scale. Specifically, I proposed a hybrid method to predict future input-output (IO) tables by combining the traditional RAS method with a Deep Neural Network (DNN) model. The DNN model can significantly improve the performance with the R2 increased from 0.6412 and 0.5271 to 0.8726 and 0.7893 when estimating IO tables one year and five years later, respectively. The prediction results can help to fill the data gap in relevant IO studies. I also addressed some of the data resolution challenges in SCP research. I started with a specific question on how to map environmental impacts (i.e., air pollution in this study) of production and consumption in high spatial-temporal resolution at the urban scale. I developed a machine learning model to infer the distribution of PM2.5 concentration in Beijing at 1 km by 1 km and 1-hour resolution by using the mobile monitoring data collected from a ride-hailing fleet with low-cost sensors. The model was able to show both short- and long-term variations of PM2.5 concentration and identify local pollution hotspots. Compared with a benchmark model that only used data from stationary monitoring sites, my model showed significant improvement with the R2 increased from 0.56 to 0.80 and Root Mean Square Error decreased from 12.6 to 8.1 μg/m3. These results demonstrated the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring. Lastly, I examined the relationship between all-cause mortality and individual risk factors by adopting more flexible machine learning models as a proof of concept for the potential impacts of the consumption and production systems on social well-being at a micro-level. Specifically, I used the survival tree and random survival forests to detect, analyze, and visualize complex interactions between individual risk factors and all-cause mortality. Based on these models, I identified the most important indicators and examined the associations between these indicators and all-cause mortality by constructing multi-dimensional heatmaps. The method used in this study can significantly improve the flexibility of the model construction for survival data.