Abstract
This paper examines the interaction of different policies used to control two types of agricultural pollution. Pollution control policy is efficient when both pollution types are controlled by taxes, although a tax increase on one type of pollution can increase the quantity of another type of pollution if farm inputs are substitutes. However, if one of the pollutions is controlled by a local emissions trading scheme, and another pollution type is taxed, then the pollution type which is taxed becomes less responsive to a change in its own tax levels. This policy scenario results in inefficient levels of environmental pollution outcomes unless the cap for the local emissions trading scheme is constantly being shifted in response to the tax.
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Notes
This assumes that consumers are unaffected by the farmer’s choice of output, because they would source farm products from international markets if they were not available locally.
The assumption of a linear relationship between inputs and pollution levels is made to simplify the mathematics, but is not fundamental to the story. If \(\Phi ^0_j = 0\) pollution doubles when an input doubles, but the damage done by nutrient pollution can increase at a much faster rate due to the convexity of the damage function. Thus, the model allows for arbitrary non-linear relationships between the damage done by nutrient pollution and the amount of an input, which is the key concern when finding an optimal policy. If \(\Phi ^0_j \ne 0\) the pollution emission function can to some extent capture the threshold effects that occur in practice when inputs are increased, as the marginal effect can be much larger than the average effect. In practice, threshold-style non-linear effects may be important for nutrient pollution. Land has a natural capacity to absorb and process a certain amount of nutrient waste, and once this threshold is exceeded additional nutrient waste flows much more rapidly into waterways. This point is discussed further in Sect. 4.
A similar example could focus on the emissions from different breeds of cows, or different sized cows. The amount of methane generated by enteric fermentation can vary significantly across cow breeds and feed-types (Grobler et al. 2014).
Since \(P_\mathrm{N}\) is zero, \(\frac{\mathrm{d N}^+}{\mathrm{d} P_\mathrm{G}}=\sum _{j\in \mathbf {J}}\sum _{i\in \mathbf {I}^j}\frac{\partial {\rm N}_{i,j}^*}{\partial P_\mathrm{G}}\), which is negative as \(\frac{\partial {\rm N}_{i,j}^*}{\partial P_\mathrm{G}}<0\) for all farmers.
Experimental NTS schemes may also be more popular than experimental tax schemes as the permits can easily be given away so the net cost to the farming community is zero.
The coefficients of the quadratic are sufficiently complex that it is not possible to find a simple analytical expression for the conditions when a switch from one land use to another increases N leaching when \(P_\mathrm{G}\) increases. An analytical expression is available in the case that there is a single input: see Yeo et al. (2013).
This assumption is not strictly necessary. In general, if \(x_i\) is different for different farmers, then the switching price \(P_{Gi}^{*}\) will be different for different farmers. If some farmers have identical \(x_i\), then several farmers will switch from one activity to another simultaneously, and the consequent change in output and/or prices will reflect the simultaneous but discrete change by all affected farmers.
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Acknowledgements
We are grateful for the funding received from the Royal Society of New Zealand and the National Science Foundation (NSF) under Grant No. 1210213. This research is made possible through the support provided by Motu Economics and Public Policy in Wellington, New Zealand and the University of California, Davis Outreach and International Programs Seed Grant. We would also like to thank Debbie Niemeier for her support and would especially thank Suzi Kerr and Michael Springborn for their valuable guidance, suggestions, and comments.
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Appendices
Appendix 1: Additional results from Sect. 3.1.1
The solution to Eq. 14 presented below is for the case that the farmer ceases using input 2 before input 1 as \(P_\mathrm{G}\) increases. The solution is
where
Since optimal emissions are \(E^{*}_{ij} = \Phi ^{0}_j + \Phi ^{1}_{ij} \underline{\theta }^*_{i,j}\), the relationship between optimal emissions and pollution prices is also piecewise linear and the derivative matrix \(\frac{\partial E^{*}_{ij}}{\partial P_\mathrm{E}}\) is:
The proofs of Propositions 2 and 3 come from expanding and inspecting the derivative matrix \(\frac{\partial E^{*}_{ij}}{\partial P_\mathrm{E}}\). The proof of Proposition 1 has two parts. When \(P_\mathrm{G} < P_1\), \(\frac{\partial {\rm N}_{i,j}^*}{\partial P_\mathrm{N}}\) and \(\frac{\partial \mathrm{GHG}_{i,j}^*}{\partial P_\mathrm{G}}\) are negative because the matrices A and hence \(A^{-1}\) are negative definite. When \(P_1 \le P_\mathrm{G} \le P_2\) and only one input \((\theta ^1_{ij})\) is used, inspection of Eq. 25 shows the derivatives are negative as \(\alpha ^{11}_{j} <0\).
Appendix 2: Proof that \(\frac{{\rm dGHG}^*}{{\rm d}P_{\rm G}} <0\) when there is an NTS
Proof.
since \(\frac{\partial P_\mathrm{N}}{\partial P_\mathrm{G}}=-\frac{\sum _{j\in \mathbf {J}}\sum _{i\in \mathbf {I}^j}\frac{\partial {\rm N}^*_{i,j}}{\partial P_\mathrm{G}}}{\sum _{j\in \mathbf {J}}\sum _{i\in \mathbf {I}^j}\frac{\partial {\rm N}^*_{i,j}}{\partial P_\mathrm{N}}}\) and \(\frac{\partial {\rm N}_{i,j}^*}{\partial P_\mathrm{G}}=\frac{\partial {\rm GHG}_{i,j}^*}{\partial P_\mathrm{N}}\)\(\forall i\).
As the denominator in Eq. (26) is negative, \(\frac{\mathrm{dGHG}^*}{\mathrm{d}P_\mathrm{G}}<0\) if the numerator is positive. The proof is done by induction by considering how the numerator changes when moving from M farmers to \(M+1\) farmers. First, we show that for any individual farmer s, \(\frac{\partial \mathrm{GHG}_{s,j}^*}{\partial P_\mathrm{G}}\frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{N}}\ge \left( \frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{G}}\right) ^2\). Then, we prove
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1.
if \(\left( \sum _{i=1}^M\frac{\partial \mathrm{GHG}_{i,j}^*}{\partial P_\mathrm{G}}\right) \left( \sum _{i=1}^M\frac{\partial {\rm N}_{i,j}^*}{\partial P_\mathrm{N}}\right) -\left( \sum _{i=1}^M\frac{\partial {\rm N}_{i,j}^*}{\partial P_\mathrm{G}}\right) ^2\ge 0\); and
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2.
if \(\frac{\partial \mathrm{GHG}_{s,j}^*}{\partial P_\mathrm{G}}\frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{N}}\ge \left( \frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{G}}\right) ^2\) for the additional \(M+1\) farmer s; then
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3.
\(\left( \sum _{i=1}^{M+1}\frac{\partial \mathrm{GHG}_{i,j}^*}{\partial P_\mathrm{G}}\right) \left( \sum _{i=1}^{M+1}\frac{\partial {\rm N}_{i,j}^*}{\partial P_\mathrm{N}}\right) -\left( \sum _{i=1}^{M+1}\frac{\partial {\rm N}_{i,j}^*}{\partial P_\mathrm{G}}\right) ^2\ge 0\).
Stage 1
We show that for any farmer s, \(\frac{\partial \mathrm{GHG}_{s,j}^*}{\partial P_\mathrm{G}}\frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{N}}\ge \left( \frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{G}}\right) ^2\) whether the farmer uses 1 or 2 inputs. Consider a farmer that uses only 1 input (input 2). From (18), \(\frac{\partial {\rm N}^*_{s,j}}{\partial P_\mathrm{N}}=\frac{(\phi ^{\rm N2}_j)^2}{2\alpha ^{22}_jx_sP_j}\); \(\frac{\partial \mathrm{GHG}^*_{s,j}}{\partial P_\mathrm{G}}=\frac{(\phi ^{\rm G2}_j)^2}{2\alpha ^{22}_jx_sP_j}\); and \(\frac{\partial {\rm N}^*_{s,j}}{\partial P_\mathrm{G}}=\frac{\phi ^{\rm N2}_j\phi ^{\rm G2}_j}{2\alpha ^{22}_jx_sP_j}\). Hence,
Second, consider a farmer who uses both inputs. Using the expressions for \(\frac{\partial \mathrm{GHG}_{s,j}^*}{\partial P_\mathrm{G}}\), \(\frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{N}}\), and \(\frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{G}}\) from Eq. 17, it can be shown that
Hence, whether farmer s uses one or two inputs \(\frac{\partial \mathrm{GHG}_{s,j}^*}{\partial P_\mathrm{G}}\frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{N}}\ge \left( \frac{\partial {\rm N}_{s,j}^*}{\partial P_\mathrm{G}}\right) ^2\).
Stage 2
To prove the second stage, suppose for some collection of M farmers,
\(\left( \sum _{i=1}^M\frac{\partial \mathrm{GHG}_{i,j}^*}{\partial P_\mathrm{G}}\right) \left( \sum _{i=1}^M\frac{\partial {\rm N}_{i,j^*}}{\partial P_\mathrm{N}}\right) -\left( \sum _{i=1}^M\frac{\partial {\rm N}_{i,j^*}}{\partial P_\mathrm{G}}\right) ^2\ge 0\). Let
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1.
\(A_M=\sum _{i=1}^M\frac{\partial \mathrm{GHG}_{i,j}^*}{\partial P_\mathrm{G}}\); \(B_M=\sum _{i=1}^M\frac{\partial {\rm N}^*_{i,j}}{\partial P_{\rm N}}\); and \(C_M=\sum _{i=1}^M\left( \frac{\partial {\rm N}^*_{i,j}}{\partial P_\mathrm{G}}\right) ^2\)
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2.
\(a_m=\frac{\partial \mathrm{GHG}_{s,j}^*}{\partial P_\mathrm{G}}\); \(b_m=\frac{\partial {\rm N}^*_{s,j}}{\partial P_\mathrm{N}}\); and \(c_m=\left( \frac{\partial {\rm N}^*_{s,j}}{\partial P_\mathrm{G}}\right) ^2\).
Note \(a_mA_M\ge 0\), \(b_mB_M\ge 0\), and \(c_mC_M\ge 0\), and let \(\epsilon =A_MB_M-C_M^2 \ge 0\), \(\mu = a_mb_m-c_m^2 \ge 0\). Then,
as \(\left( \frac{c_mB_M}{C_Mb_m}+\frac{C_Mb_m}{c_mB_M}\right)\)\(\ge 2\) and \(\epsilon\), \(\mu \ge 0\).
Hence, if a collection of M farmers satisfies condition 1, any set of \(M+1\) farmers also satisfies it. Since we showed condition 1 holds when \(M=1\), by induction it must hold for any arbitrary set of farmers. Thus, the numerator of equation 19 is non-negative and so \(\frac{\mathrm{d GHG}^*}{\mathrm{d} P_\mathrm{G}} \le 0\).
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Yeo, BL., Coleman, A. Taxes versus emissions trading system: evaluating environmental policies that affect multiple types of pollution. Environ Econ Policy Stud 21, 141–169 (2019). https://doi.org/10.1007/s10018-018-0225-x
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DOI: https://doi.org/10.1007/s10018-018-0225-x