Essays on Environmental, Energy and Land Economics in China

This dissertation consists of three essays involving environmental pollution, electricity consumption, and farmland leasing in China. These economic analyses are linked by their inclusion of institutional changes which have occurred in China over the past half-century. The first essay examines the effects of environmental pollution and institutional abatement targets on real average housing prices in China. The Spatial Difference-In-Difference model shows that the overall effect of 2006 SO2 institutional abatement targets is to increase real average housing prices across provinces. The changes in both emissions of sulfur dioxide and industrial wastewater discharges have negative impacts on the change of real average housing prices. Essay two considers one of the most important issues in electricity consumption research, namely, the electricity consumption function. This research demonstrates that metropolitan electricity consumption is a function of economic output and electricity consumption habits along with the electricity demand management reform. Finally, the third essay develops a theoretical model to identify optimal farmland contracts. Under complete information, a fixed-rent contract is the optimal institutional arrangement from land lessor’s perspective. Conversely, a share contract is the best choice for land lessor under incomplete information. The empirical results show that the farmer who leases farmland to external individuals has a lower probability of choosing a fixed-rent contract. However, the farmer who leases farmland to internal individuals is less likely to choose a share contract

Causal Inference under Network Interference Using a Mixture of Randomized Experiments

In randomized experiments, the classic stable unit treatment value assumption (SUTVA) states that the outcome for one experimental unit does not depend on the treatment assigned to other units. However, the SUTVA assumption is often violated in applications such as online marketplaces and social networks where units interfere with each other. We consider the estimation of the average treatment effect in a network interference model using a mixed randomization design that combines two commonly used experimental methods: Bernoulli randomized design, where treatment is independently assigned for each individual unit, and cluster-based design, where treatment is assigned at an aggregate level. Essentially, a mixed randomization experiment runs these two designs simultaneously, allowing it to better measure the effect of network interference. We propose an unbiased estimator for the average treatment effect under the mixed design and show the variance of the estimator is bounded by $O({d^2}n^{-1}p^{-1})$ where $d$ is the maximum degree of the network, $n$ is the network size, and $p$ is the probability of treatment. We also establish a lower bound of $\Omega(d^{1.5}n^{-1}p^{-1})$ for the variance of any mixed design. For a family of sparse networks characterized by a growth constant $\kappa \leq d$, we improve the upper bound to $O({\kappa^7 d}n^{-1}p^{-1})$. Furthermore, when interference weights on the edges of the network are unknown, we propose a weight-invariant design that achieves a variance bound of $O({d^3}n^{-1}p^{-1})$

Optimal dynamic electricity consumption function estimation: an institutional experimental evidence from Guangzhou, China (1949-2016)

This research demonstrates from a dynamic optimal perspective that electricity consumption for a metropolitan area is a function of economic output, electricity consumption habits, and electricity demand management reform. The empirical results include: (1) an unidirectional Granger causality exists linking economic output to electricity consumption; (2) given electricity consumption habits under the context of the electricity demand management reform, an economic output increase of 1% results in the increase of electricity consumption by 0.22%, and (3), after demand management has been implemented, economic output continues to increase electricity consumption, but at a lower rate than prior to reform. These empirical results imply that the ‘conservation hypothesis’ is upheld over the long-run at the regional level in Guangzhou from 1949 to 2016

Robust Control of Crane with Perturbations

In the presence of persistent perturbations in both unactuated and actuated dynamics of crane systems, an observer-based robust control method is proposed, which achieves the objective of trolley positioning and cargo swing suppression. By dealing with the unactuated and unknown perturbation as an augmented state variable, the system dynamics are transformed into a quasi-chain-of-integrators form based on which a reduced-order augmented-state observer is established to recover the perturbations appearing in the unactuated dynamics. A novel sliding manifold is constructed to improve the robust performance of the control system, and a linear control law is presented to make the state variables stay on the manifold in the presence of perturbations in unactuated dynamics. A Lyapunov function candidate is constructed, and the entire closed-loop system is proved rigorously to be exponentially stable at the equilibrium point. The effectiveness and robustness of the proposed observer-based robust controller are verified by numerical simulation results

Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph

Business Intelligence (BI) is crucial in modern enterprises and billion-dollar business. Traditionally, technical experts like database administrators would manually prepare BI-models (e.g., in star or snowflake schemas) that join tables in data warehouses, before less-technical business users can run analytics using end-user dashboarding tools. However, the popularity of self-service BI (e.g., Tableau and Power-BI) in recent years creates a strong demand for less technical end-users to build BI-models themselves. We develop an Auto-BI system that can accurately predict BI models given a set of input tables, using a principled graph-based optimization problem we propose called \textit{k-Min-Cost-Arborescence} (k-MCA), which holistically considers both local join prediction and global schema-graph structures, leveraging a graph-theoretical structure called \textit{arborescence}. While we prove k-MCA is intractable and inapproximate in general, we develop novel algorithms that can solve k-MCA optimally, which is shown to be efficient in practice with sub-second latency and can scale to the largest BI-models we encounter (with close to 100 tables). Auto-BI is rigorously evaluated on a unique dataset with over 100K real BI models we harvested, as well as on 4 popular TPC benchmarks. It is shown to be both efficient and accurate, achieving over 0.9 F1-score on both real and synthetic benchmarks.Comment: full version of a paper accepted to VLDB 202

What drives housing consumption in China? Based on a dynamic optimal general equilibrium model and spatial panel data analysis

Abstract. This paper examines the housing sales in China from 2004 to 2015 utilizing an optimal dynamic general equilibrium theoretical framework combined with a macroeconomic model. The spatial panel econometric empirical results suggest that housing prices and economic growth have increased housing sales in China. However, since house is considered as a special commodity in China, and unemployment show negative impacts on housing sales.Keywords. Energy use, Housing values, Optimal dynamic general equilibrium, Spatial panel econometrics, China.JEL. Q41, R31, E10