Speakers: Alejandro Echeverria and Paris Charitatos

Building Electrification decision making tool - Alejandro Echeverria

Property owners are facing increased pressure from regulators and consumers to reduce emissions and electrify. Policies such as NYC’s Local Law 97, Boston’s BERDO, Washington DC’s Climate and Energy Action Plan, set aggressive emission targets for new and existing buildings. Building developers need a tool to assess the value of electrification. Such a decision making tool for building electrification will be critical for new as well as existing construction. Currently working on developing this tool for new construction and in future also plan to include existing construction / retrofits. The model has been finalized and currently only focuses on new construction applications in NYC​ and hopefully will be able to extend to other regions in future.​

The role of ride-splitting in transit deserts - Paris Charitatos
Ride-sharing services (e.g., Uber Pool, Lyft Share) were introduced as an affordable on-demand mobility option while simultaneously contributing to less congestion and reduced CO2 emissions. One of the advantages of pooling is that it might be a solution for transit-dependent population who live in areas with limited or non-existent public transportation service, also known as transit deserts. Although there is a growing literature about TNC, few studies examine explicitly ride-splitting and its role as a complementary service in underserved communities. Evidence from TNC data in Chicago are utilized in spatial and regression analysis to study the temporal patterns and the relationships between demographic characteristics and the use of pooling. Also, we use General Transit Feed Specification (GTFS) data from the Chicago Transportation Authority (CTA) to define transit deserts and reveal if ride-sharing can fill the transit gap. The results of this research could potentially help policymakers understand the socio-spatial context and promote the right incentives for ride-splitting to the people who need it the most.

Speaker: Dongxiao Niu

Abstract: This study estimates the capitalization of climate shocks on commercial real estate owned and operated by sophisticated investors. We focus on Hurricane Sandy and Hurricane Harvey to quantify the price impacts of climate shocks on commercial buildings in the U.S. We find clear evidence of a decline in transaction prices in hurricane-damaged areas after the hurricane made landfall, compared to unaffected areas. Moreover, we observe that properties in locations with stronger market fundamentals in the local area or in the small portfolios owned by local investors show smaller discounts for the climate risk. These results demonstrate that the “climate shock discount” in real estate prices depends on the “replaceability” of the current asset (and associated location) in investors’ choice set. If a property is less replaceable in the investor’s location choice set or asset choice set (portfolio), the investors are willing to claim a smaller price discount because they do not have enough other good alternatives to choose from. We also create an index using Google search to rank investors with respect to their “marketing greenness” and document that these “green” investors are likely to claim a larger price discount for properties at higher climate risks.

Online social and information networks play an important role in risk perception and economic decision-making. However, analyzing the cognitive and behavioral effects of online social learning is challenging because of the absence of scalable network metrics to capture the topic-specific information flows. We propose a data-driven approach to model the social media information connectivity across space, leveraging high granularity Twitter data and Elastic-Net augmented variance decomposition. Our method produces the directionality and intensity of information spillovers between each location pair (state/MSA) during the two-month period surrounding Hurricane Ida landfall. We find that the structure of the hurricane information network is influenced by both universal factors (such as geographical distance, friend linkages, and socio-demographics) and hurricane-specific factors (such as hurricane severity and climate risk). Our information network will be useful for understanding the drivers and barriers of climate information transmission and for modeling the effects of online social learning on offline climate adaptation behaviors.

Speaker: Yichun Fan

Ride-hailing services, provided by Transportation Network Companies, have changed the way urbanites moving across the city in the past decade. Regardless of its prevalence in major cities around the world, micro-evidence linking ride-hailing services and economic activities beyond the transportation domain is however lacking. Leveraging a temporally and spatially varying taxation program on ride-hailing services implemented in the city of Chicago, this paper examines how consumers adjust their ride-hailing use, as well as its impact on consumer activities such as visiting restaurants and bars. I find that in contrast to ride-hailing trips for commuting purposes, consumption-oriented trips are less elastic to the increase in ride-hailing cost, implying a persistent demand for flexible mobility for off-work consumer activities.

Speaker: Binzhe Wang

bstract: Given that lifecycle greenhouse gases (GHGs) emissions from wasted food are comparable to that of entire road transport, managing excessive food demand is essential for achieving climate change mitigation goals. A textbook solution to GHGs emissions is levying a corrective tax, but there is limited empirical evidence on how and why households would respond to such taxes. In this paper, I study the effect of a small food waste tax (on average $0.05 per KG) in South Korea on grocery purchase behavior, which is one of the primary household abatement strategies. By exploiting a plausibly exogenous variation in each municipality’s food waste tax due to the central government’s mandate, I first show that the policy reduces per household annual grocery purchases by 44KG (or 5.3%), where the effect is driven by a reduction in perishable items. The second part of the paper investigates the explanations as to why such a small tax has such a large effect on grocery purchases by testing theoretical predictions from a household production model a la Becker (1965). Using pre-tax period grocery demand elasticity, I find that the price effect of the tax explains only 5% of the 44KG reduction while the rest can be explained by the “movement of the curve” effect. To pin down the driver of the demand shift, I test whether the tax induces (1) substitution to relatively cheaper production input, (2) reduction in nutrition intake, and (3) productivity gain. In the last part of the paper, I show that the net GHGs reduction from the tax translates into removing 576,000 passenger vehicles from the road even after considering leakage by linking grocery purchases to food-item-specific GHGs emissions intensity. The findings indicate that a small tax on food waste can be an effective climate change mitigation tool by inducing environmentally advantageous upstream behavior changes.

Speaker: Seunghoon Lee