Optimizing Pollution Policies in Rural China

Taggart Bonham
Mar 11, 2018 · 7 min read

Policies that reduce further emissions and remediate land provide cost-effective, long-term solutions.

Ever since spending my summers living in the countryside of 峭崎鎮 (a village in the Jiangsu province) teaching English, I’ve been extremely interested in rural pollution in China. I’ve written for many years on the issue and wanted to use principles from system dynamics to model the spread of pollution in order to simulate the impact of various pollution tax policies on the land.

Project Overview

CONTEXT

MODELING APPROACH

Smartphone Demand

Generate smartphone demand growth to equilibrium with oscillations using a Bass Diffusion Curve, and a sinusoidal product cycle to represent current dynamics of product rollout in the electronics industry.

Supply Chain Dynamics

Create supply chain dynamics using stock-generated flows focusing on factory building and inventory management. This structure simulates how the number of factories grows to fulfill the increasing smartphone demand, allowing us to model the resulting pollution.

Spread of Pollution

Model pollution on the land with main chain of arable and polluted land driven by a stock adjustment process. This process shows us how pollution spreads over the land wrt time.

Policy Implementation

Starting at 2018, if policies are enabled, a tax equal to the ratio between the percent of the polluted hectares and the pollution threshold will be levied. This tax will generate money to be spent on the various policies we seek to try.


TAX POLICIES

Given that pollution data in China is notoriously hard to obtain, the first order conditions (rate of pollution spread, specific policy cost per hectare, and duration of remediation) are based off of analogous values from BRIC countries that underwent similar modernization processes.

Base Case

Demand for the fictitious phone product follows a logistic growth curve and plateaus in 2030 to simulate market penetration of a new electronic product. The demand oscillates sinusoidally such that every 2 years each user buys a new phone to match a typical product cycle.

Through modeling the industrialization of a village during a technology boom, the number of factories converges to a plateau as demand becomes effectively constant. The number of arable hectares exponentially decays until the number of factories becomes constant, at which point it decays linearly. The model behavior shows that given no policy interventions, the current land will continue deteriorating to its theoretical limit.

Emission Reduction Policies

While emission reduction policies can halt further emissions at great cost, they cannot undo the historical pollution damage to the land.

Remediation Policies

While these policies temporarily reverse the pollution, the remediated hectares quickly become polluted again. Thus, this policy is not stable over time as t­he progress is quickly undone.


OPTIMIZING POLICY COMBINATION

Optimization

The model shows that the optimal policy allocates 28% of the tax spending towards remediation and 72% toward emission reduction policies. While this specific balance is the result of the assumptions ­­­behavior is observed regardless of the relative costs per hectare between the two strategies. Thus, while the precise numbers are irrelevant, the optimal policy combination is likely a balanced approach of emission reduction and remediation.

The model structure of land allocation is therefore amended to represent this optimal hybrid approach.

Optimal Tax Policy

As this policy both halts further emissions and revitalizes polluted land, it increases arable land to a stable long-term level at minimal cost.


CONCLUSIONS

FURTHER READING

  • To interact with the model by varying the parameters and learn more about tax allocation, visit the live model.
  • For previous research, read the technical overview.

REFERENCES & ACKNOWLEDGMENTS

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Thomas E.. Vollmann, William L.. Berry, and D. Clay Whybark. Manufacturing Planning and Control Systems. Irwin/McGraw-Hill, 1997.

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Angela R. Bielefeldt, PhD, PE, “Bioremediation: Phytoremediation,” last modified 2008, PDF.

Chan, Leong, and Tugrul Daim. “Exploring the Impact of Technology Foresight Studies on Innovation: Case of BRIC Countries.” Futures 44.6 (2012): 618–630.

Taggart Bonham

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