Achieving ambitious climate targets: is it economical for New Zealand to invest in agricultural GHG mitigation?

Our analysis uses an agri-environmental economic model based on Daigneault et al (2018) to estimate the economic costs of implementing land-based GHG emissions reduction practices at the national scale in New Zealand between 2015 and 2050. The spatially explicit model is a nonlinear mathematical programming model of New Zealand land use that is delineated at the farm-parcel level. Similar versions of the model have been used to assess GHG mitigation policy (Daigneault et al 2018), climate change impacts (Monge et al 2018), land restoration (Daigneault et al 2017b), erosion control (Fernandez and Daigneault 2017), and nutrient management (Daigneault et al 2017a).

In the model, total economic returns from the New Zealand agriculture sector, calculated as annual net farm revenue $\left(\pi \right),$ are measured as:

$$\begin{eqnarray}&&\begin{array}{l}\pi =\,\sum _{r,s,l,e,m}\left\{P{A}_{r,s,l,e,m}+{Y}_{r,s,l,e,m}\,-{X}_{r,s,l,e,m}\right.\\ \,\times \,3\left.\left[{\omega }_{r,s,l,e,m}^{live}+\,{\omega }_{r,s,l,e,m}^{vc}+\,{\omega }_{r,s,l,e,m}^{fc}+\,\tau {\gamma }_{i,r,s,l,e}^{env}\right]\,\right\},\end{array}\end{eqnarray} \tag{ 1 }$$

where P is the product output price, A is the agricultural product output quantity, Y is other gross income earned by landowners (e.g. grazing fees), X is the area of specific farm-activity, and ω^live, ω^vc, ω^fc are the respective livestock, variable, and fixed input costs, τ is an environmental tax (if applicable), and γ^env is an environmental output coefficient. Summing the revenue and costs of production across all regions (r), soil types (s), land covers (l), enterprises (e), and land management options (m) yields the total net revue for the geographical area of concern. Methods for estimating other costs of implementing land-based GHG mitigation practices are described below.

The model tracks the flow of several environmental factors (E_i) from more than a dozen different land uses, including GHG emissions and sequestration, and freshwater contaminants. Per hectare values are specified via the parameter γ^env, and as with economic returns, can vary by region, soil type, land cover, and enterprise. Summing over the area of all land use activities yields the aggregate environmental output from land-based activities for New Zealand:

$$\begin{eqnarray}&&\sum _{r,s,l,e,m}{\gamma }_{i,r,s,l,e,m}^{env}{X}_{r,s,l,e,m}=\,{E}_{i}.\end{eqnarray} \tag{ 2 }$$

Equation (2) specifies environmental impacts under current land use. In our analysis, we consider applying a range of GHG mitigation practices and/or emissions reduction targets on agricultural sector land. To describe environmental impacts under such a policy, we amend equation (2) to:

$$\begin{eqnarray}&&\begin{array}{l}\sum _{r,s,l,e,m\,}\left\{{\gamma }_{i,r,s,l,e,m}^{{\prime} }\left({X}_{r,s,l,e,m}-{Z}_{r,s,l,e,m}\right)\right.\\ \,\left.+\,{\psi }_{i,r,s,l,e,m}^{env}{Z}_{r,s,l,e,m}\right\}=\,{E}_{i}^{{\prime} },\end{array}\end{eqnarray} \tag{ 3 }$$

where Z is the area of the land in which specific GHG mitigation practices are applied. The parameter γ' specifies the environmental impacts of land use after accounting for the mitigation, while ψ^env describes the impact of the practices on the environmental factors. In this paper, we assume that γ' ≤ γ^env, as GHG mitigation practices reduce total environmental effects by (a) reducing impacts per unit of land use, and (b) through their own biophysical processes that reduce GHG emissions or sequester carbon. The environmental impact after implementing mitigation practices, E_i', is equal to or smaller than the impact without these practices, E_i, so that the mitigation in impact i achieved by implementing a specified set of mitigation practices is E_i – E_i'. As Z represents the area that implements on-farm GHG mitigation production, which is typically at an increased cost relative to the industry standard practices, it also has a non-positive effect on the net economic returns estimated in equation (1).

The mitigation potential of implementing farm-specific GHG mitigation is quantified as a percentage change in GHG emissions (or sequestration) relative to a base case in which farms implement the regional industry standard practices for their enterprise (not all practices are suitable for all farms based on their current land use and farming system). There has been extensive research on the range of practices and level of effectiveness of such farm-specific mitigation options in New Zealand for both GHG emissions (Adler et al 2013, Reisinger and Ledgard 2013, Vibart et al 2015, Dorner and Kerr 2017, Daigneault et al 2017b) as well as freshwater contaminants (Manderson et al 2007, Monaghan et al 2007, Daigneault and Elliot 2017) in addition to the more detailed and updated set presented in this analysis. Most of this literature indicates that effectiveness of a given GHG mitigation practice will depend on factors such as the farm's physical characteristics (e.g. soil, slope, and rainfall), specific practices implemented (e.g. fertilizer regime, stocking, and planting type), and technical expertise. A more detailed description of the model's mathematical formulation, data, and calibration procedure is in the supplementary material, available online at stacks.iop.org/ERL/14/124064/mmedia.

This analysis attempts to determine how well New Zealand can meet its emission reduction goals through adopting agricultural mitigation options that are currently available and/or that are under development. Implementing the mitigation options will impose costs on the agricultural sector, and those costs may not be born evenly across farming enterprises. The impact of each option on greenhouse gas emissions are depicted in table 1, and more details on the assumptions related to each are provided in section 2 of the supplementary material. The descriptions include both the effectiveness and costs of each practice to explore the trade-off between achieving mitigation goals and the economic impacts on the agricultural sector.

The practice-based scenarios include each practice individually, and then we bundled practices into three broader categories: (1) mitigation options currently available; (2) emerging mitigation technologies; and (3) emerging mitigation technologies that achieve high effectiveness. Table 1 identifies the practices considered in this analysis, summarized by ease of adoption, maximum mitigation potential if adopted by all eligible farms in NZ, and the average mitigation cost.

This analysis investigates a range of policy scenarios that were compared against a no mitigation baseline land use and emissions scenario to assess both how the costs to New Zealand varied with different domestic GHG emissions reduction targets and the optimal mix of practices to achieve that target. The first set of scenarios are practice-based, which estimate the technical potential that each individual practice could achieve. The second set are target-based, where the agri-environmental model is constrained to achieve a gross GHG limit at least cost in 10% reduction increments.

As the emerging technologies are still under development and will not likely be commercialized for at least 7–10 years, there is high uncertainty about both the overall effectiveness they achieve and their cost. To address this uncertainty, we performed a sensitivity analysis for the methane inhibitor and vaccine that includes a range of efficacies and costs, which are listed in table S3. These ranges are based on values currently found in published literature (e.g. Cotter et al 2015, Reisinger et al 2018). Note that most of the baseline assumptions fall into the mid-range of estimates, with the exception of the CH₄ vaccine cost, which is based on recent research specifically from the region (Cotter et al 2015). In addition, we performed sensitivity analyzes to isolate the potential effect that excluding afforestation and the composition of the future mitigation bundles may have on the cost of meeting mitigation targets.

The agri-environmental land use model baseline is calibrated to empirical estimates of NZ land use, stock numbers, productivity, net farm returns, and GHG emissions from 2015. The recursive-dynamic model is then run to 2050 in 5 year time steps, following historical trends in land productivity, animal numbers, and GHG emission intensities but holding land use area and financial returns fixed. As a result, we estimate that in 2050, NZ's 26.3 Mha will produce $11.7 billion of net farm returns, 34.2 MtCO₂e of gross GHG emissions, and 24.8 MtCO₂e of forest carbon sequestration, thereby resulting in 9.4 MtCO₂e of net GHG emissions (table 2). A bulk of the economic returns and gross emissions are estimated to be produced by the dairy, sheep, and beef sectors (80% and 96%, respectively). All of the mitigation scenarios are measured against this no mitigation baseline.

The practice based scenarios focused on estimating the relative cost and effectiveness if all eligible landowners implemented a specific set of GHG mitigation practices. Results indicate that there is a large spread in both the cost and effectiveness of various practices modeled for this analysis (table 3), with practices producing total gross GHG mitigation estimates ranging from 0.5 to 21.0 MtCO₂e/yr, or a 2% to 62% reduction from the baseline. However, the cost of individual practices can vary in magnitude, thereby having an important effect on likelihood of implementation. For example, CH₄ inhibitors are estimated to be highly effective as they could reduce agricultural GHGs by 38%–46%, but at an average cost of $220/tCO₂e, they are more expensive than 75% of the options considered. On the other hand, using targeted urine patch treatments on flat pasture only costs $2/tCO₂e but has a minimal impact on total agricultural GHGs because it can only mitigate N₂O from dairy farms. Practices with costs of $50/tCO₂e or less that could reduce NZ's GHG emissions by at least 20% compared to the baseline include afforestation and the methane vaccine. Furthermore, we estimate that organic farming and all of the mitigation practice combination bundles that do not include the CH₄ inhibitor could reduce emissions by at least another 20% but cost in the range of $50 to $100/tCO₂e. All of the other practices are estimated to cost more than $100/tCO₂e or are not large enough in scope to reduce agricultural emissions by more than 5%. However, collectively, if farmers implement several practices on their land with abatement costs of under $100/tCO₂e, then they have the potential to reduce nearly all of the country's baseline GHG emissions (figure 1).

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Additional analysis indicates that the sheep and beef sector would bear the majority of estimated costs from the mitigation options (figure S1 is available online at stacks.iop.org/ERL/14/124064/mmedia). While this is logical given the total area and emissions that the sector produces, it is also not nearly as profitable as the dairy or deer sectors. Thus, many New Zealand farmers could find it difficult to remain viable without additional compensation.

The target-based scenarios analyzed the potential impacts of achieving specific GHG reduction goals but allowed farmers to collectively select the optimal mix of practices at least possible cost. To estimate this, we ran the model at 10% reduction increments, up until gross agricultural GHG emission targets were 90% below the 2050 baseline.

Average and marginal abatement costs increase nonlinearly with more stringent targets, as expected (table 4, figure 2). The degree that practices are implemented also closely follows findings from the practice-based scenarios: low-cost options such as afforestation, organic farming and administering the CH₄ vaccine to dairy cows are implemented until their total mitigation potential is exhausted (up to 30% reduction). After that, more farmers move towards administering the vaccine to sheep and beef; adopting bundles of mitigation practices on dairy farms, specifically the high efficiency combination of the CH₄ vaccine and N inhibitor; and leaving some of the least productive pasture fallow (40%–60% reduction). For a 70% reduction in gross GHGs, a majority of the target could be achieved if all pastoral farmers adopted a mix of high efficiency combinations of future practices, afforestation, or stock exclusion; however about 5.5 Mha of pasture would also have to be left fallow. Once the target reaches 80% reduction below the baseline, at least 7 Mha of NZ's pasture used for sheep and beef may become fallow because all of the more cost-effective options are exhausted. Interestingly, dairy farmers only adopt the more costly but effective mitigation combo that includes both the vaccine and CH₄ inhibitor for the most stringent target (90% reduction). As with the practice-based scenarios, at least two-thirds of the costs, on average, are incurred by sheep and beef farmers.

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The average costs of achieving a gross GHG reduction target of 10% to 50% range from $14 to $76/tCO₂e, while the marginal costs for the same targets are between $15 and $162/tCO₂e. These figures are well within the range of estimates of achieving low emissions pathways and arguably are on the lower side of estimated costs for NZ's energy and transportation costs to meet similar GHG targets (Ballingall and Pambudi 2018, Vivid Economics et al 2018). Thus, it appears that if emerging mitigation technologies realize their potential in the next 30 years, then NZ agriculture could be a major source of mitigation.

Setting national targets that focus on reducing gross agricultural GHG emission also has a strong effect on net GHGs. This is because afforestation of a mix of pine plantations and native trees has the potential to increase forest carbon sequestration by an average of 17 tCO₂e/ha/yr, and nearly all scenarios result in planting the maximum 1 Mha on marginal pasture land. As a result, NZ's land use sector could have net zero GHG emissions even if gross agricultural GHG mitigation targets are relatively low.

Detailed sensitivity analysis highlights the relative effect that key assumptions such as the cost and effectiveness (C/E) of emerging technologies have on the results. For this study, we conducted the following scenarios to evaluate the potential range of impacts of achieving a 50% agricultural GHG reduction target:

• Scen 0: Original assumptions.
• Scen 1: No Highly Effective combo.
• Scen 2: Current practices only.
• Scen 3: Low CH₄ inhibitor and vaccine C/E.
• Scen 4: Medium CH₄ inhibitor and vaccine C/E.
• Scen 5: High CH₄ inhibitor and vaccine C/E
• Scen 6: High CH₄ inhibitor and vaccine C, low E.
• Scen 7: Low CH₄ inhibitor and vaccine C, high E.
• Scen 8: No afforestation.
• Scen 9: Future practices only.
• Scen 10: Fallow land only.

Note that all but Scen 0 excluded the 'future with high efficiency' mitigation combinations because of the large uncertainty about whether the optimistic reduction potential could technically be achieved. Estimates indicate that both the total cost of the policy and the distribution of practices employed to achieve the target are indeed sensitive to the model assumptions, particularly if the CH₄ vaccine costs are closer to the medium to high range (figure 3, table S4). In this case, landowners would no longer choose to administer the vaccine but instead would convert to organic, employ more costly mitigation combos, or let some of their land go fallow. In addition, if afforestation is not considered a feasible option but the CH₄ vaccine was still relatively cheap (Scen 8), then farmers would likely administer a mix of emerging mitigation options to achieve the 50% target. Encouraging farmers to implement a suite of current and emerging practices will reduce the cost of meeting a 50% reduction target by at least 31% ($790 million yr⁻¹) compared to the scenario where the only option is to let land go fallow (Scen 10). Interestingly, restricting mitigation practices to just emerging technologies could actually cost more than all other sensitivity cases (Scen 9), primarily because of the high cost of the CH₄ inhibitor. However, if these options can be develeloped at relatively low cost and be highly efficient (Scen 7), then the 50% GHG reduction target could be achieved for about $375 million yr⁻¹, equivalent to a 3% reduction from the baseline, and 71% less costly than Scen 0.

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