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.
Through tax benefits and lax environmental standards, local governments incentivize the building of factories to grow their economy. The resulting factory pollution contaminates land via wastewater. Polluted land becomes infertile and uninhabitable, killing residents and the local farming industry. As current research suggests that more than 70% of China’s soil could have problems, China nears the “red line” of 120 million arable hectares needed to sustain the population. Therefore, rural pollution poses a threat to the long-term viability of the country. We seek to find the optimal tax policy that reverses pollution at minimal cost.
Using historical smartphone sales, we generated demand for a hypothetical smartphone product. We developed a supply chain in a fictitious village to fulfill the demand, and modeled the resultant pollution’s effect on the land. Imposing tax policies on this model structure enables us to simulate the effects on pollution over time, leading us to an optimal tax policy.
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.
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.
Throughout all scenarios, the model is run from 1980 to 2050 to simulate the effect of industrialization on the land. If a regulatory policy is enacted, it begins in 2018 and continues until the end of the simulation.
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.
The effects of pollution are simulated given no policy intervention from 1980 to 2050.
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
The effects of pollution are simulated from 1980 to 2050 with a tax beginning in 2018 fully allocated to emission reduction policies.
While emission reduction policies can halt further emissions at great cost, they cannot undo the historical pollution damage to the land.
The effects of pollution are simulated from 1980 to 2050 with a tax beginning in 2018 fully allocated toward remediation.
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
The optimal policy combination maximizes arable hectares with minimal tax cost generated by the following function:
Testing different combinations of spending on remediation versus emission reduction policies beginning in 2018, we plot the resulting weighted arable acreage at 2050.
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
The effects of pollution are simulated from 1980 to 2050 with a tax beginning in 2018 allocated 72% toward emission reduction and 28% toward remediation policies.
As this policy both halts further emissions and revitalizes polluted land, it increases arable land to a stable long-term level at minimal cost.
Emission reduction halts the inflow to polluted acreage but fails to reverse historical damage. Remediation policies revitalize land but don’t stop further emissions, thus land continues to become polluted. Instituting these single-focused pollution policies is neither effective nor cost-efficient. While we cannot know the precise optimal balance given the assumptions made about the first order conditions, the model shows that hybrid policies are strictly better long-term solutions. Therefore, further research into exact precise remediation and emission reduction costs would enable us to remove assumptions and further fine-tune the optimal policy balance.
Taggart Bonham | Product Manager
Check out my resume, some of my past projects, or contact me.
- 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
Special thanks to Steve Peterson for all his insight, guidance, and encouragement along the way!
Josh Chin and Brian Spegele, “China Details Vast Extent of Soil Pollution,” The Wall Street Journal (New York), April 17, 2014.
Economy, Elizabeth C. “Congressional Testimony: China’s Environmental Challenges, Council on Foreign Relations.”
Wang, Jin. “The economic impact of special economic zones: Evidence from Chinese municipalities.” Journal of development economics 101 (2013): 133–147.
Han, Dongmei, Matthew J. Currell, and Guoliang Cao. “Deep challenges for China’s war on water pollution.” Environmental Pollution 218 (2016): 1222–1233.
Shao, Wanyun. “Understanding Chinese Environmental Risk Perceptions from 1995–2015.” China’s Urbanization and Socioeconomic Impact. Springer, Singapore, 2017. 125–144.
Long, Sha, et al. “Pollution control and cost analysis of wastewater treatment at industrial parks in Taihu and Haihe water basins, China.” Journal of Cleaner Production 172 (2018): 2435–2442.
Monastersky, Richard. “China’s air pollution chokes crop growth.” Science News 155.13 (1999): 197–197.
Monden, Yasuhiro. “Lean Management of Global Supply Chain: Dynamic Combination Model of Market, Product Life-Cycle, Product Design, and Supply Chain.” Lean Management of Global Supply Chain. 2016. 3–46.
Yang, Yan, and Eric Williams. “Logistic model-based forecast of sales and generation of obsolete computers in the US.” Technological Forecasting and Social Change 76.8 (2009): 1105–1114.
Tseng, Fang-Mei, Ya-Lin Liu, and Hsiang-Hsun Wu. “Market Penetration Among Competitive Innovation Products: The case of the Smartphone Operating System.” Journal of Engineering and Technology Management 32 (2014): 40–59.
Thomas E.. Vollmann, William L.. Berry, and D. Clay Whybark. Manufacturing Planning and Control Systems. Irwin/McGraw-Hill, 1997.
Angerhofer, Bernhard J., and Marios C. Angelides. “System Dynamics Modelling in Supply Chain Management: Research Review.” Simulation Conference, 2000. Proceedings. Winter. Vol. 1. IEEE, 2000.
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.