class: center, middle, inverse, title-slide .title[ # Industry level climate strategies: ] .subtitle[ ## Computational Experiments in Coalition Formation within the Maritime Shipping Sector ] .author[ ### Dr. James Nolan and Feryel Lassoued ] .institute[ ### <i> University Of Saskatchewan: Department of Agriculture And Resource Economics ] .date[ ### 2022-05-09 ] --- # Overview 1. Introduction 2. The stability of industry-level climate coalition model: A Pollution Game of Environmental Governance within Maritime Shipping 3. Numerical Results of base simulation 4. Coalition stability in the presence of dynamic Taxation Schemes 5. Conclusions and limitations --- # Introduction .meduim[ Ocean shipping emitted `\(\small 1,076 Mton\)` of `\(\small CO_2\)` in 2018, accounting for `\(\small 2.89\%\)` of the total global `\(\small CO_2\)` emissions for that year.However, with its rapidly growing fleet size and the tripping of world trade from `\(\small 2020\)` to `\(\small 2050\)`, emissions are projected to reach `\(\small 90-130\%\)` of `\(\small 2008\)` emissions by `\(\small 2050\)` for a range of plausible long-term economic and energy scenarios (*Faber et al. (2020)*). To mitigate, the IMO pledges to *halve* industry emissions by mid-century relative to its `\(\small 2008\)` level and mandates a cut of `\(\small 40\%\)` by `\(\small 2030\)` and `\(\small 70\%\)` by `\(\small 2050\)` in carbon emissions per transport work, compared with `\(\small 2008\)` (*IMO (2018)*). Albeit, the regulatory agency is relying on *trust* and *voluntary participation without governance or any enforcement policies* <blockquote> <i><b>Within such a complex industrial and regulatory framework, environmental governance will only likely be achieved through industry-level coalition formation.</i></b> </blockquote> [<font color="blue">The Clean Shipping Coalition (CSC) initiative</font>](http://www.cleanshipping.org/about/) encompasses more than `\(\small 90\%\)` of the global shipping fleet. The coalition is proposing [<font color="blue">speed control as an operational measures</font>](https://seas-at-risk.org/press-releases/shipping-climate-talks-in-the-slow-lane-over-speed-reduction-measures/) for tackling climate change and is calling for market-based mechanisms to establish a `\(\small USD~ 5~ B\)` fund towards implementing a zero-carbon-emission ship.(*Sean (2019)*). <blockquote> <b><i>Speed limits on ships could save billions in lower ship fuel bills while enabling the industry to play a full part in climate change</i></b> </blockquote> <blockquote> <b><i>The challenge as we go forward is to ensure that this most straight-forward of approaches is taken up and implemented in such a way that all ships contribute speed-related emission savings</i></b> </blockquote> ] ??? if you click on "The Clean Shipping Coalition (CSC) initiative it will take you to their official website --- # Research Problem .pull-left[ .panelset[.panel[.panel-name[Research Question] - How can we design and analyze the emissions dynamics that exist between slow steaming and free-riding incentives, into an applicable game-theoretical framework? - How do we solve for an optimal emissions abatement path as a non-cooperative game among heterogeneous agents with different cost structures? - Are there stable coalitions that occur in the absence of enforcement policies? - Does the design of taxation policy influence incentive structures as well as the stability of industry-level emissions coalitions? ].panel[.panel-name[Research Objectives] - Investigate the likelihood of considerable policy free-riding - identify the drivers for stable environmental governance in maritime shipping ]] ] .pull-right[  ] --- # A Climate Game of Environmental Governance .meduim[ <b><i>Stability of Climate Coalitions (STACO): Country-level simulation of environmental governance</i></b> - Theoretical Foundation of our model - A research project on the formation and stability of international climate agreements. - Originated by Wageningen University (Ekko van Ierland, Hans-Peter Weikard, Rob Dellink) and Michael Finus (Hagen University/University of Exeter) and Advanced through several PhD projects and staff research - A hybrid integrated assessment model that links climate change to economic growth through a non-cooperative game-theoretical 2 stage game aimed at exploring all possible coalitions structure between 12 heterogeneous world regions <b><i>Stability of Industry-level Climate Coalitions: Ocean Shipping Market level simulation of environmental governance</i></b> A two-stage (simulation and optimization) cartel formation game for heterogeneous agents in the maritime industry: - Stage 1: Membership Decision: A simulation framework - Stage 2: Abatement Strategy Decision: A dynamic optimization framework <b><i>Assumptions and divergence from STACO</i></b> - The coalition’s main objective is to reduce the business as usual emissions path over the model’s planning horizon T per the policy objectives through speed control - A one-shot game, ie membership decisions don’t change throughout the planning horizon - While we incorporate GDP to calibrate the model, we restructure STACO to reflect firm-level decision making. We don't assume industry-wide monetary benefits from abatement and lower pollution stocks - The marketplace is governed by 5 heterogeneous companies, with equal market shares, each with their own (parametrized) benefit and cost structure regarding environmental abatement. ] --- # Business As Usual Emissions Framework .panelset[ .panel[ .panel-name[Simulated Market Transport Demand] .pull-left[ .meduim[ The simulation framework's market demand at time `\(\scriptsize t\)` in `\(\scriptsize TEU\)` be transported between Europe and Asia (*Parry et al, 2018*) `$$\scriptsize Y_t^{Europe-Asia} = (\frac{GDP_t}{GDP_0})^v \times (\frac{\rho_t}{\rho_0})^η \times Y_0^{Europe-Asia}$$` such that : - `\(\scriptsize v=0.8%\)` : Income elasticity for container products - `\(\scriptsize η= -0.7%\)`: Own-price elasticity of demand - `\(\scriptsize \rho_{t} = 822~\ USD/TEU\)`: Average Freight rate at t Containerized Trade for Europe–Far East service (Million TEU) is adapted from UNCTAD (2021) from 2018 to 2021. We assume shipping rates are known for the round-trip and generally constant over the planning horizon `\(\scriptsize \frac{\rho_t}{\rho_0} = 1\)` : `$$\scriptsize Y_t^{Europe-Asia} = \biggl(\frac{GDP_t}{GDP_{2021}}\biggr)^{0.8} ~ Y_{2021}^{Europe-Asia}~ \forall t \geq 2021$$` ] ] .pull-right[ <div id="htmlwidget-66c162a3c8f86a838f76" style="width:95%;height:504px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-66c162a3c8f86a838f76">{"x":{"visdat":{"4e5a4102a128":["function () ","plotlyVisDat"]},"cur_data":"4e5a4102a128","attrs":{"4e5a4102a128":{"alpha_stroke":1,"sizes":[10,100],"spans":[1,20],"x":{},"y":{},"name":"Containership transport work in TEU","type":"scatter","mode":"lines+markers","line":{"width":1,"dash":"dot"},"symbol":"o","inherit":true}},"layout":{"margin":{"b":40,"l":60,"t":25,"r":10},"font":{"size":10},"plot_bgcolor":"rgba(0, 0, 0, 0)","paper_bgcolor":"rgba(0, 0, 0, 0)","fig_bgcolor":"rgba(0, 0, 0, 0)","legend":{"x":0.1,"y":0.9},"title":"<b>Firm level Demand, <br> Assuming equal market shares (20%)","yaxis2":{"tickfont":{"color":"black"},"overlaying":"y","side":"right","title":"GDP"},"xaxis":{"domain":[0,1],"automargin":true,"title":"period "},"yaxis":{"domain":[0,1],"automargin":true,"title":"Containership transport work in TEU"},"hovermode":"closest","showlegend":false},"source":"A","config":{"modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"data":[{"x":[2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042],"y":[1400000,1440000,1440000,1560000,1620858.07475903,1667373.10983732,1712573.51847927,1757638.19438706,1803888.70260672,1851356.24714103,1900072.85309519,1950071.3882833,2001385.58540348,2054050.06479636,2108100.35780253,2163572.93073466,2220505.20948028,2278935.60475209,2338903.53800268,2400449.46802107,2463614.91822927,2528442.50469703,2594975.96489372,2663260.18719694,2733341.24117746],"name":"Containership transport work in TEU","type":"scatter","mode":"lines+markers","line":{"color":"rgba(31,119,180,1)","width":1,"dash":"dot"},"marker":{"color":"rgba(31,119,180,1)","symbol":"circle","line":{"color":"rgba(31,119,180,1)"}},"error_y":{"color":"rgba(31,119,180,1)"},"error_x":{"color":"rgba(31,119,180,1)"},"xaxis":"x","yaxis":"y","frame":null}],"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script> ] ] .panel[.panel-name[ Shipping firm's optimization problem] .adjust_pull-left[ .small[ $$ `\begin{aligned} \scriptsize \max_{{ N_{i,t} ,V_{i,t} }} \pi_{i} = {} & \scriptsize \sum_{t=1}^{T} {(1+r)^{-t}} \bigg[~ \rho_t~ X^{Europe-Asia}_{i,t} \\ & \scriptsize - \bigg( \eta_t^{fuel}~ \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i} }\rceil~ d~ \phi_i~ (V_{i,t})^2 \bigg) \\ & \scriptsize - \bigg( \eta_t^{MGO}~ \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i}}\rceil ~ d~ F_A ~ \frac{1}{V_{i,t}} \bigg) \\ & \scriptsize- \bigg( FC_{i,t} ~ N_{i,t} \bigg) \bigg] \forall i =1,.., N^{ firms} \end{aligned}` $$ subject to: - Firm Level Demand: `\(\scriptsize X_{i,t}^{Europe-Asia}= s_{i} Y_t^{Europe-Asia}\)` - Vessels Operational Constraint: `$$\scriptsize \tau = 270*24=6480~ hours~ per~ year.$$` - Firm Level Supply: `\begin{equation} \begin{cases} \scriptsize N_{i,t} \geq N_{{min}_{i,t}}^{vessel} = \lceil{ \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i} }\rceil \frac{ \bigg( \frac{d }{V_{i,t}} +t_{port} \bigg)} {\tau } \rceil} \\ \scriptsize N_{i,t} \in Z^+ \\ \scriptsize V_i^{min} \leq V_{i,t} \leq V_i^{max} \end{cases} \end{equation}` - Energy Efficiency and Fuel Consumption: `\begin{equation} \begin{cases} \scriptsize \phi_i = SFOC_0^M \times EL^M \times PS^M \times 10^{-6} \times (\frac{1}{V_i^{s}})^3 \\ \scriptsize F_A = SFOC^A \times EL^A \times PS^A \times 10^{-6}\\ \end{cases} \end{equation}` ] ] .adjust_pull-right[ .small[ | Parameters | Notation | Firm 1 | Firm 2 | Firm 3 |Firm 4 |Firm 5| |:-------------------|:------------|:------------|:------------|:------------|:------------|:------------| | Vessel capacity `\(\scriptsize (TEU)\)` | `\(\scriptsize k_i\)` | 14,000 |12,000| 10,000 |8,000 | 6,000 | | Main engine power `\(\scriptsize (kW)\)` | `\(\scriptsize PS_i^M\)` | 89,700 | 82,100|74,000| 68,500 | 57,100 | | Auxiliary engine power `\(\scriptsize (kW)\)` | `\(\scriptsize PS_i^A\)` | 14,000 |14,000 | 12,000 | 12,000 | 12,900 | | Design speed `\(\scriptsize (knots)\)` | `\(\scriptsize V^s_i\)` | 25 | 25 | 25 |25 | 25 | | Specific fuel oil consumption `\(\scriptsize (g/kWh)\)` | `\(\scriptsize SFOC_{0,i}^M\)` | 175 | 133 | 159|143| 114 | | Specific fuel oil consumption `\(\scriptsize (g/kWh)\)` | `\(\scriptsize SFOC_{0,i}^A\)` | 32 |28|24 | 24 | 26 | | Fixed Operating Cost `\(\scriptsize (million~ USD/year)\)` | `\(\scriptsize FC_{i}\)` | 18.25 | 16.74|15.24 | 13.73 | 12.22 | | Main Engine Energy Efficiency `\(\scriptsize ((ton/hour).knot^-3 )\)`| `\(\scriptsize \phi_{i}\)` |8.03712e-07 |5.5906816e-07 | 6.0241920e-07 |5.0152960e-07| 3.3328128e-07| | Auxiliary Engine Energy Efficiency `\(\scriptsize(ton/hour)\)` | `\(\scriptsize F_{A,i}\)`| 0.0002240 |0.0001960 |0.0001440 |0.0001440 |0.0001677| - Cycle Distance: `\(\scriptsize d = 23,000 ~ nm\)` - Average time Port : `\(\scriptsize T_{port} = 10 ~ days\)` - Average Engine load factor (main engine): `\(\scriptsize EL^M= 0.8\%\)` - Average Engine load factor (auxiliary engine): `\(\scriptsize EL^A= 0.5\%\)` - Maximum Vessel speed : `\(\scriptsize V_i^{max} = 28 ~ knots\)` - Minimum Vessel speed : `\(\scriptsize V_i^{min} = 12 ~knots\)` - Design Speed: `\(\scriptsize V^s_i = 25 knots\)` Data collected from *Doudnikoff and Lacoste (2014)* and *Brahimi, Cheaitou, Cariou, and Feillet (2021)* ] ] ] .panel[.panel-name[ BAU Emissions levels] .meduim[ Carbon BAU emission levels `\(\overline{e^{CO_2}_{i,t}}\)` are derived per the following: .pull-left[ `$$\scriptsize \overline{e^{CO_2}_{i,t}} [ tonnes _{(CO_2)} ] = \epsilon^{CO_2 } \bigg[ \frac{tonnes _{(CO_2)}}{tonnes_{(fuel)}} \bigg] \times F_{i,t} [tonnes_{(fuel)}] \\ \scriptsize = \epsilon^{CO_2 }_{fuel} \times \bigg( \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i} }\rceil \times d \times \phi_i \times (V_{i,t}^{BAU})^2 \bigg)+ \epsilon^{CO_2 }_{MGO} \times \bigg( \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i} }\rceil \times F_A \times \frac{d}{V_{i,t}^{BAU}} \bigg) \\ \scriptsize = \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i} }\rceil \times d \bigg( \epsilon^{CO_2 }_{fuel} \times \phi_i \times (V_{i,t}^{BAU})^2 + \epsilon^{CO_2 }_{MGO} \times \frac{F_A}{V_{i,t}^{BAU}} \bigg)$$` Sulfur BAU emission levels `\(\overline{e^{SO_x}_{i,t}}\)` are computed per the following: : `$$\scriptsize \overline{e^{SO_x}_{i,t}} [ tonnes _{(SO_x)} ] = \epsilon^{SO_x } \bigg[ \frac{tonnes _{(SO_x )}}{tonnes_{(fuel)}} \bigg] \times F_{i,t} [tonnes_{(fuel)}] \\ \scriptsize = \epsilon^{SO_x }_{fuel} \times \bigg( \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i} }\rceil \times d \times \phi_i \times (V_{i,t}^{BAU})^2 \bigg)+ \epsilon^{SO_x }_{MGO} \times \bigg( \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i} }\rceil \times F_A \times \frac{d}{V_{i,t}^{BAU}} \bigg) \\ \scriptsize = \lceil{\frac{X^{Europe-Asia}_{i,t}} {k_i} }\rceil \times d \times\bigg( \epsilon^{SO_x }_{fuel} \times \phi_i \times (V_{i,t}^{BAU})^2 + \epsilon^{SO_x }_{MGO} \times \frac{F_A}{V_{i,t}^{BAU}} \bigg)$$` ] .pull-right[ <table class=" lightable-classic lightable-striped" style='font-family: "Arial Narrow", "Source Sans Pro", sans-serif; margin-left: auto; margin-right: auto;'> <thead> <tr> <th style="text-align:left;"> fuel type </th> <th style="text-align:left;"> HFO (3.55%) </th> <th style="text-align:left;"> ULSFO (0.5%) </th> <th style="text-align:left;"> MGO (0.1%) </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Global 20 Ports Average fuel Prices </td> <td style="text-align:left;"> $422.5 / tonne </td> <td style="text-align:left;"> 525.5 $/tonne </td> <td style="text-align:left;"> 597 $/tonne </td> </tr> <tr> <td style="text-align:left;"> Carbon Emission Factor </td> <td style="text-align:left;"> 3.114 (g/g of Fuel) </td> <td style="text-align:left;"> 3.206 (g/g of Fuel) </td> <td style="text-align:left;"> 3.206 (g/g of Fuel) </td> </tr> <tr> <td style="text-align:left;"> Sulfur Emission Factor </td> <td style="text-align:left;"> 0.07 (g/g of Fuel) </td> <td style="text-align:left;"> 0.01 (g/g of Fuel) </td> <td style="text-align:left;"> 0.002 (g/g of Fuel) </td> </tr> </tbody> </table> - For this presentation, we are assuming vessels are burning HFO fuel. ] ] ]] --- # Numeric Results of the BAU Emission Framework .panelset[ .panel[.panel-name[Optimum Vessel Speed] .pull-left[ .meduim[ <table class="table table-striped" style="font-size: 10px; width: auto !important; margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;"> Firm </th> <th style="text-align:right;"> Optimal Vessel Speed </th> <th style="text-align:left;"> Net Present Value </th> <th style="text-align:left;"> Discounted Fixed Operating Costs </th> <th style="text-align:left;"> Discounted Fuel Consumption Costs </th> <th style="text-align:left;"> Carbon Emissions </th> <th style="text-align:left;"> Sulfur Emmissions </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> 5 </td> <td style="text-align:right;"> 21.78 </td> <td style="text-align:left;"> $5.29 B </td> <td style="text-align:left;"> $15.79 B </td> <td style="text-align:left;"> $10.54 B </td> <td style="text-align:left;"> 100.52 Mt </td> <td style="text-align:left;"> 2.15 Mt </td> </tr> <tr> <td style="text-align:left;"> 4 </td> <td style="text-align:right;"> 16.67 </td> <td style="text-align:left;"> $8.36 B </td> <td style="text-align:left;"> $16.63 B </td> <td style="text-align:left;"> $6.64 B </td> <td style="text-align:left;"> 62.9 Mt </td> <td style="text-align:left;"> 1.33 Mt </td> </tr> <tr> <td style="text-align:left;"> 1 </td> <td style="text-align:right;"> 16.67 </td> <td style="text-align:left;"> $12.48 B </td> <td style="text-align:left;"> $12.65 B </td> <td style="text-align:left;"> $6.49 B </td> <td style="text-align:left;"> 61.62 Mt </td> <td style="text-align:left;"> 1.31 Mt </td> </tr> <tr> <td style="text-align:left;"> 2 </td> <td style="text-align:right;"> 16.67 </td> <td style="text-align:left;"> $11.75 B </td> <td style="text-align:left;"> $13.55 B </td> <td style="text-align:left;"> $6.32 B </td> <td style="text-align:left;"> 59.94 Mt </td> <td style="text-align:left;"> 1.27 Mt </td> </tr> <tr> <td style="text-align:left;"> 3 </td> <td style="text-align:right;"> 16.67 </td> <td style="text-align:left;"> $10.74 B </td> <td style="text-align:left;"> $14.78 B </td> <td style="text-align:left;"> $6.10 B </td> <td style="text-align:left;"> 57.97 Mt </td> <td style="text-align:left;"> 1.23 Mt </td> </tr> </tbody> </table> __Key Observations__: - Emissions are driven by fuel consumption. - Sulfur and Carbon follow the same trend - Firm 5, The firm with the lowest vessel size depicts the **dirty firm **in our model. - The graphs show how sensitive emissions are to speed levels _Our analysis is limited by data availability for firm-level characteristics. 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] ] .panel[.panel-name[Industry Abatement Target ] .tiny_adjust_pull-left[ .meduim[ <table class="table table-striped" style="font-size: 11px; margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;"> Speed Levels for firm 1 to 5 </th> <th style="text-align:left;"> Industry NPV </th> <th style="text-align:left;"> Industry Carbon Emissions </th> <th style="text-align:left;"> Industry Sulfur Emissions </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> 16.66675, 16.66668, 16.66689, 16.66672, 21.78033 </td> <td style="text-align:left;"> $48.62 B </td> <td style="text-align:left;"> 342.95 Mt </td> <td style="text-align:left;"> 7.29 Mt </td> </tr> </tbody> </table> - Simulating the various speed reduction from the Business As Usual, allow us to investigate the potential carbon and sulfur abatement along with the industry's sustained average cost of abatement. - For the simulated market, A `\(\small 29\%\)` Speed reduction from BAU induces a `\(\small 43\%\)` and a `\(\small 48\%\)` decrease in carbon and sulfur emissions at an incurred `\(\small 30\%\)` loss in industry profits over the planning horizon. <blockquote> It costs the simulated containership industry `\(\small USD ~ 4.57 ~ B\)` to cut `\(\small 147.47 ~ Mt ~ CO_2\)` and `\(\small 3.499 ~ Mt ~ SOx\)` when compared to BAU. 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] ] ] --- # Industry Level Climate Coalition of Speed Control .panelset[.panel[.panel-name[Incentive To Cheat] .double_adjust_pull-left[ .small[ - Analyse the incentive to cheat `\(\scriptsize (ITC)\)` on an abatement policy of a `\(\scriptsize CO_2\)` emission cap, within an **ALl Singleton** and A **Grand Coalition** market structure. The `\(\scriptsize (ITC)\)` is affected by the payoff function. It quantifies the opportunity cost of slow steaming as an abatement policy. `$$\scriptsize ITC_{i\in Grand ~ Coalition ~ market } = \frac{ \Pi^{*}_{i \in Coalition}- \Pi^{*}_{i,BAU} }{\Pi^{*}_{i,BAU}}$$` `$$\scriptsize ITC_{i \in All ~ Singleton ~ market } = \frac{\Pi^{*}_{i, Singleton}- \Pi^{*}_{i,BAU}}{\Pi^{*}_{i,BAU}}$$` - Feasible carbon abatement percentages for the firms and the industry have been derived from the previous speed reduction policy analysis. The range over the planning horizon ranged from `\(\scriptsize 1\%\)` to `\(\scriptsize 40\%\)` when compared to BAU. Whether setting up an emission cap or mandating an average speed reduction, Singletons will re-optimise their operations to reach the same vessel speeds owing to cost trade-offs. - By equating the marginal benefits to its marginal cost and optimizing the sum of its members' net present values, the coalition assigns heterogeneous speed per its' signatories' characteristics following an industry carbon cap. For all simulated emission targets, the industry's cost of abatement is smaller with a grand coalition market structure than with an all singleton market. The coalition allows us to reach the goal cost-effectively. - `\(\scriptsize ITC_{i, Grand ~ Coalition ~ market }\)` fluctuates with the emission target, as it's driven the signatories' contribution to the coalitions' total emissions. ] ] .double_adjust_pull-right[ <div id="htmlwidget-c1235a0ecc5e443380fc" style="width:100%;height:504px;" class="plotly html-widget"></div> <script type="application/json" 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] ] ] ??? - The cases where only a subset of firms in the industry form a climate coalition can of course be considered as well, however, we believe it will extensively lengthen the presentation of the results without adding further insight into our analysis. Thus, we only consider the stability of the grand coalition. --- # Coalition Stability Analysis Assuming compliance, with the emission threshold, we investigate members' incentive to change membership $$\scriptsize ITCM_{i} (\$) = \Pi_{i, Singelton} - \Pi_{i \in Coalition}$$ `$$\scriptsize ITCM_{i} (\%) = \frac{ \Pi_{i, Singelton} - \Pi_{i \in Coalition }}{\Pi_{i \in Coalition }}$$` --- # Internalising Pollution Externalities .meduim[ Consider a dynamic tax policy in a feedback form which depends on the industry's fuel consumption, intending to induce a stable coalition. the tax rate charged at time t will be given by `\(\small \tau_t^{CO_2}\)` and depends on the state variable, the stock of Fuel consumption. Let `\(\small \alpha \geq 0\)`, the dynamic carbon tax follow the path: `$$\small \tau^{CO_2}_{t+1} =\tau^{CO_2}_{t =1} + \bigg( \alpha \times \frac{Fuel~Consumption_t^{CO_2 }}{ Fuel~Consumption_{t=1}^{CO_2 } } \bigg)$$` Another Design : `$$\small \tau^{CO_2}_{t+1} = \tau^{CO_2}_{t =1} + \bigg( \alpha \times \frac{Fuel~Consumption_{t+1}^{CO_2 } - Fuel~Consumption_{t}^{CO_2}}{ Fuel~Consumption_t^{CO_2 } } \bigg)$$` - The case where `\(\alpha=0\)` corresponds to a 'myopic' environmental regulator who does not react to changes in the industry's emissions. - The introduction of the dynamic taxation schems renders the pollution game a non-cooperative game, when evaluating the Incentive to cheat (ITC) and the (ITCM) .. ] --- # Inputs are welcomed .meduim[ - Given the chosen vessels, which market shares are appropriate to reflect the extent of the current market - Should we tax through fuel consumption or emission levels - Is it better to set an abatement target per period or over the planning horizon. - Should account for ECA areas in the model and speed differentiation - Should we assume compliance with the sulfur cap and switch to ULSFO - Coalition resource sharing for the environment to mitigate the trade-offs of the additional vessels (vessel pooling) ] --- # References .meduim[ - Faber, S., Hanayama, S., Zhang, S., Pereda, P., Comer, B., Hauerhof, E., & Kosaka, H. (2020). Fourth IMO GHG study 2020. London: International Maritime Organization - IMO. (2018). Initial IMO GHG strategy. Retrieved 2021-07-20, from https://www.imo.org/en/MediaCentre/HotTopics/Pages/Reducing-greenhouse-gas-emissions-from-ships.aspx - Doudnikoff, M., & Lacoste, R. (2014). Effect of a speed reduction of containerships in response to higher energy costs in sulphur emission control areas. Transportation Research Part D: Transport and Environment, 28, 51–61 - Brahimi, N., Cheaitou, A., Cariou, P., & Feillet, D. (2021). An exact algorithm for the single liner service design problem with speed optimisation. International Journal of Production Research, 59(22), 6809–6832 - Parry, I., Heine, M. D., Kizzier, K., & Smith, T. (2018). Carbon taxation for international maritime fuels: Assessing the options. International Monetary Fund ]