The Comparative Analysis of Carbon Pricing Policies on Canadian Northwest Territories’ Economy under Different Climate Change Scenarios

Abstract

Policymakers in the Northwest Territories have introduced carbon pricing as a strategy to reduce fossil fuel consumption and CO₂ emissions across various population segments and industries. This indirect approach, chosen for its acceptability, aims to influence behavior rather than directly limit carbon-intensive products. The main purpose of this study was to evaluate the economic and ecological impacts of this policy and its alignment with intended objectives. Using a CGE macroeconomic model incorporating economic structural and behavioral equations, we assessed the policy’s effects on NWT’s economy in general and on a subset of its key sectors. We also incorporated a few observed and simulated climate data for diverse climate change scenarios. The estimated results revealed that climate variables, especially precipitation, significantly influenced sectors like agriculture, construction, and manufacturing. The standardized precipitation evapotranspiration index (SPEI), which encompasses both temperature and precipitation, notably impacted the agriculture, oil, and gas sectors. However, temperature alone showed limited significance, except in the oil and gas sector. The simulation results indicated that, while carbon pricing reduced economic contributions of fossil fuel sector, household rebates could counteract these effects of the economic growth of NWT. Our findings offer valuable insights for shaping NWT’s environmental policies, aligning them with Canada’s goal of net-zero CO₂ emissions by 2050.

1. Introduction

The global challenge of climate change, driven primarily by carbon emissions, has prompted nations worldwide to seek effective mitigation strategies. These emissions, responsible for the accumulation of greenhouse gases, have been linked to drastic climatic anomalies, such as extreme temperature fluctuations, rising sea levels, and unpredictable food supply chains [1,2]. The Northwest Territories (NWT) of Canada, located at higher latitudes, exemplifies the acute effects of this phenomenon by experiencing a warming rate that is triple the global average. The NWT is already confronting both ecological and infrastructural challenges, from melting permafrost to deteriorating infrastructure [3,4]. Given the region’s economic structure being heavily anchored in natural resource exploitation, the implications of these changes are profound.

In response to global initiatives, notably the 2015 Paris Agreement, Canada embarked on a mission to implement carbon pricing systems across all its provinces and territories by 2018. This initiative aimed to curtail annual GHG emissions by 30% from 2005 figures by 2030 [3,5]. The NWT, acknowledging its distinct challenges, responded by launching its 2030 Climate Change Strategic Framework (CCSF) in 2019, targeting a reduction in CO₂ emissions [6]. The economic ramifications of carbon pricing have been a focal point of numerous studies, each offering insights into its multifaceted impacts across various regions.

Devarajan et al. (2009) and Dissanayake et.al. (2018) [7,8] employed computable general equilibrium (CGE) models to study South Africa, Sri Lanka, and Pakistan. Their findings underscored the positive influence of carbon pricing on the welfare of citizens and the broader economy.

A study by Pradan et al. (2017) [9] highlighted the differential impacts of carbon pricing, noting that China’s terms of trade effects resulted in higher carbon prices compared with India. Zhang et al. (2019) [10] further explored China’s scenario, revealing that carbon pricing adversely affected competitiveness. However, the detrimental effects were somewhat mitigated in China’s less-developed provinces.

Campagnolo and Davide (2019) [11] delved into the intersection of carbon pricing and global sustainable development goals. Using a dynamic CGE model, they assessed the potential of carbon pricing to mitigate carbon emissions in several countries with varied GHG reduction commitments. Their research suggested that nations with lenient commitments, like Egypt, might not derive substantial benefits from carbon pricing.

Parry (1995) and Yamazaki (2017) [12,13] both touched upon a few potential pitfalls of carbon pricing. Their findings suggested, that while a carbon tax could increase revenue and enhance efficiency, there was a risk of deterring employment and investment, especially in sectors directly impacted by the pricing.

Few studies have ventured into the long-term effects of carbon pricing. Notable exceptions include Goulder (1995) and Pereira et al. (2016) [14,15]. The latter identified three potential dividends of carbon pricing: environmental conservation, employment and growth stimulation, and a reduced public debt-to-GDP ratio.

The often-overlooked distributional consequences of carbon pricing have been highlighted in studies like Olale et al.’s 2019 [16] study, where the potential adverse effects on specific sectors were emphasized, even when exempt from carbon pricing.

In synthesizing the above literature, it becomes evident that while there are overarching themes, the effects of carbon pricing are highly contextual, varying based on the varied structures of regional economies and the policies used. This underscores the need for tailored analyses, such as the one required for the Northwest Territories (NWT) (a unique northern economy characterized by its sparse population, reliance on natural capital, and vulnerability to climate influences).

This study aimed to bridge this gap by developing a dynamic computable general equilibrium (CGE) model tailored for the NWT. We assessed the economic and ecological implications of carbon pricing in the NWT under various climate change scenarios and evaluated the effectiveness of the policy in achieving the GNWT’s net-zero emission objectives. Through this comprehensive analysis, we hoped to provide valuable insights for policymakers and contribute to the broader understanding of carbon pricing’s role in climate change mitigation.

2. Methodological Approach

2.1. Rationale for the Adopted Computable General Equilibrium Model

Computable general equilibrium (CGE) models offer a streamlined representation of the economic landscape, elucidating the interplay of economic variables in response to external stimuli. Contrasted with macrolevel traditional models, like input–output and linear programming frameworks, the CGE model uniquely leverages the concurrent adjustment of prices and values to attain optimization and equilibrium positions. Specifically, these conditions encompass zero profit, market clearance, and income equilibrium.

While models such as input–output and linear programming treat prices and values as external givens, in the CGE model, both are conceptualized as endogenous components. The primary ambition when explaining and elaborating the CGE model lies in pinpointing these endogenous variables—essentially, equilibrium prices, production values, and incomes—predicated on a given set of parameters and external variables.

The choice of a dynamic computable general equilibrium (CGE) model, is motivated by its efficacy in evaluating outcomes of introducing carbon pricing, as evidenced in the preliminary literature overview. Applying the dynamic CGE model to the NWT allows us to encapsulate the comprehensive ramifications of policy enactments, especially the distinctive consequences stemming from the adoption of carbon tax policies.

Distinct from econometric models, which necessitate extensive statistical and sequential data, CGE models are relatively parsimonious in their data needs, principally relying on reference-year statistics. Elasticity-related data can be assimilated from extant econometric analyses. Furthermore, CGE model parameters are gleaned via the calibration technique, anchored on reference-year data and statistics.

2.2. Data Sources for the Computable General Equilibrium Model

There are four types of data used in this study: (1) Basic input data required for the preparation of social accounting matrix (SAM) tables for the NWT’s economy. The data consist of (a) the input–output table of the NWT in 2016, which were obtained from Statistics Canada, and (b) the basic economic data and statistical data such as government subsidies to households and tariffs, which were obtained from NWT Statistics Canada. The developed NWT SAM table is composed of 23 sectors by aggregating and disaggregating the IO table based on data availability and sector characteristics (23 industries and 23 commodities); (2) the national accounts or territorial accounts; (3) government budgetary accounts; and (4) balance of payments and trade statistics.

The supply-use table (another way of organizing the IO table) provides information on the production sectors of the economy, showing detailed inter-industry linkages and the contribution made by primary factors of production to each sector. For example, we know how much gas, labor, chemicals, etc., serve as inputs into the diamond industry. The macroeconomic accounts provide a breakdown of aggregate demand according to consumption, investment, government spending, and the international sector (exports and imports). The trade account usually covers data on the destination and product composition of exports and imports. These have been reconciled with the territorial/provincial accounts as well as with the standard input–output table. Such an integration means that the resulting SAM, for example, shows not only how much gas, labor, and chemicals go into the diamond industry but how much of each of those inputs are sourced domestically and how much are sourced from abroad and from each trading partner.

2.3. Description of the Computable General Equilibrium Model

The NWT CGE model is a comprehensive framework that encompasses various key components to analyze the economic equilibrium of a region in response to external factors, such as the introduction of a carbon tax policy [17]. This model serves as a valuable tool for evaluating the socio-economic and environmental (SEE) consequences of different policy interventions. By utilizing the NWT CGE model, we can explore the intricate relationships between policies and their respective SEE impacts, while also capturing economic shifts over specific timeframes. Through comparative analyses of scenarios with and without policy interventions, we gain valuable insights into the diverse SEE implications of various policies and their interconnected dynamics.

The NWT CGE model is structured around four key agents: households, government, investment, and the rest of the world. It encompasses a wide range of sectors and activities, including production, trade, revenue, expenditure, labor market equilibriums, and calibration. By employing customized production and consumption functions, the model can assess changes in commodity prices, industrial output, and agent consumption, enabling a comprehensive cross-sectoral examination of the impact of carbon pricing.

The foundational economic data used in this analysis are derived from a specially tailored social accounting matrix that is based on data from 2016. This matrix forms the basis for our modeling framework, allowing us to accurately represent the economic relationships within the NWT and the rest of Canada. The forthcoming subsections delve into the specifics of these blocks.

2.3.1. Production Block

The production block of the CGE model plays a crucial role in understanding the economic repercussions of environmental strategies. The value-added share (Equation (A1), Appendix A) is pivotal, as industries in the NWT would reassess their production contributions based on carbon costs embedded in value-added rates. As industries in the region source goods and services for intermediate consumption (Equation (A2), Appendix A), their patterns might alter depending on the carbon footprint of these commodities and the carbon pricing mechanisms in place. The cost structure for production, specifically the value-added price (Equation (A3), Appendix A), would integrate carbon taxes and subsidies, reflecting the region’s climate policy priorities. Furthermore, as industries adjust to carbon constraints, their demand for labor, both resident (Equation (A4) and non-resident (Equation (A8), Appendix A), and their capital requirements (Equation (A7), Appendix A) might shift, influenced by evolving green technology and practices.

Carbon pricing would also play an influential role in reshaping the pricing dynamics in NWT industries. The composite price for capital and non-resident labor (Equation (A9), Appendix A) could see fluctuations based on carbon pricing policies, impacting the overall production cost structures. The zero-profit condition (Equation (A10), Appendix A) becomes even more relevant, ensuring industries account for carbon costs while setting their prices. By aggregating costs linked with carbon-taxed inputs (Equation (A6), Appendix A) and those of intermediate consumption (Equation (A11), Appendix A), one can derive the total carbon-adjusted production cost (Equation (A12), Appendix A). Marginal costs (Equation (A13), Appendix A) would reflect the costs of additional production in a carbon-constrained environment. Finally, the supply dynamics of each industry (Equation (A14), Appendix A) and the average selling price across all outputs (Equation (A15), Appendix A) would provide insights into how NWT industries navigate production amidst carbon pricing policies and varying climate change scenarios.

2.3.2. Trade Block

The trade block of the CGE model represents a comprehensive mechanism detailing the flow of goods between regions and their associated values. In the model, interprovincial trade between NWT and the rest of Canada (ROC) is meticulously articulated to ensure balance. Equations (A16)–(A38) (Appendix A) dictate that the quantity and monetary value of commodities that NWT trades with ROC (whether imports or exports) must correspond precisely to ROC’s trade records with NWT, confirming both the physical and monetary consistency in interprovincial trade. Moreover, Equations (A29)–(A32) (Appendix A) highlight how domestic and interprovincial trade prices are shaped by import prices, and the markup industries apply. Special cases in trade are addressed by Equation (A35) (Appendix A), ensuring unique trade conditions are considered.

Internationally, when trading with the rest of the world (ROW), the model employs Equations (A42) and (A43) to set the Canadian dollar prices for imports and exports, respectively. These prices are influenced by world prices, exchange rates, and potential taxes. Additionally, the aggregate demand for a specific commodity within NWT is calculated using Equation (A44) (Appendix A), considering all demand sources, including industry, household, investment, and government spending.

2.3.3. Revenue and Expenditure Block

The revenue and expenditure block in the CGE model essentially breaks down and represents the flow of money into and out of the government’s coffers. It captures how policies, economic activities, and external factors (like climate change) affect government revenues and how the government then allocates these funds in the form of expenditures. This block is integral in understanding the financial feasibility and implications of various policies within the economy.

Equations (A45)–(A57) (Appendix A) detail the potential revenue streams for the NWT government with an emphasis on carbon pricing mechanisms. For instance, a direct carbon tax on CO₂ emissions can be represented by Equation (A45) (Appendix A). The subsequent equations might encapsulate revenues from an emissions’ trading system or the impact of climate change on traditional revenue sources like Arctic resource extraction royalties. Meanwhile, potential federal subsidies aiding NWT’s transition to a low-carbon economy or mitigating climate impacts can be modeled in the latter part of this section.

On the expenditure side, Equations (A58)–(A70) (Appendix A) outline how the NWT government might spend its funds in response to carbon pricing and climate change. Costs associated with carbon-pricing administration, infrastructure upgrades for climate adaptation, investments in green technologies, and compensation packages for stakeholders affected by these changes are examples of expenses that can be represented within these equations.

2.3.4. Dynamic Equations

In the NWT, Equation (A77) (Appendix A) is pivotal in capturing how real expenditure-side GDP at market prices evolves in response to carbon pricing. The alteration in GDP dynamics could result from shifts in industrial activity or consumption patterns due to carbon pricing. This equation leverages a lagged growth rate to gauge the current GDP in the region, thereby reflecting the territory’s macroeconomic adjustments to the carbon policy and associated climatic changes. Equations (A78) and (A79) (Appendix A) highlight labor dynamics. Given the unique labor market of the NWT, with its resident and non-resident labor force distinctions, these equations measure the potential labor shifts. These arise from changes in industry demand due to carbon pricing or from migration patterns influenced by climatic changes. Furthermore, for NWT’s industries, Equation (A80) (Appendix A) becomes central in understanding capital accumulation dynamics, especially under carbon pricing. Industries can adjust their investments based on the perceived benefits or costs of such policies. The equation gauges the present capital stock by considering the past accumulated capital, adjusting for depreciation, and incorporating the current investment. In essence, this equation depicts how industries in the NWT reallocate or modify their capital considering carbon pricing policies and varying climatic scenarios.

2.4. Linkage of the CGE Model to Climate Change Impacts

In the preceding sections, we delineated a proposed CGE model specifically designed to assess the ramifications of carbon tax policies on pivotal macroeconomic indicators, while elucidating the intricate interplay across diverse sectors foundational to the NWT economy. However, this model inherently did not factor in the dynamic influences of climate change. To bridge this gap, we formulated a novel integration of an “interior-exterior” model. This innovative approach zeroes in on the nuanced responses of specific economic variables to climate shifts, leveraging regression modeling to pinpoint the direct economic repercussions of climatic fluctuations on select economic sectors. Subsequently, the insights gleaned from these regression analyses—embodied in the derived coefficients—were seamlessly integrated into the CGE model’s production module. This ensured an accurate representation of the climate’s tangible impact on sectors where significant correlations were identified. The ensuing subsections offer a deeper dive into the mechanics of this interior–exterior model.

2.4.1. Data Sources for the Inter–Exterior Model

Our modeling, informed by macroeconomic data from 1991 to 2020, utilized climate data from Canada’s Federal Ministry of Environment and Climate Change. These data, derived from 24 advanced climate models, retroactively assessed the climate from 1850 to 2005 and projected the climate from 2006 to 2100 using IPCC greenhouse gas emission scenarios. The models’ outputs, initially between 100 km² and 250 km², were honed to 10 km × 10 km resolution through Natural Resources Canada’s observed dataset, starting from 1950 due to earlier data scarcities in Canada’s North. Key climate metrics in our economic modeling included annual temperature, precipitation, and the 12-month standardized precipitation evapotranspiration index (SPEI), a universal gauge for drought and wet periods. SPEI classifications range from no drought (SPEI > −0.5) to extreme drought (SPEI < −2). Our study spanned 1991–2019 data and 2020–2100 projections, centering on five principal NWT regions (

Table 1. Study regions.

City	Country/Region	Acronym	Latitude and Longitude
Inuvik	Beaufort	EV	68.3607° N, 133.7230° W
Norman Wells	Sahtu	VQ	65.2815° N, 126.8287° W
Yellowknife	North Slave	ZF	62.4540° N, 114.3718° W
Hay River	South Slave	SS	60.8162° N, 115.7854° W
Fort Simpson	Dehcho	FS	61.8628° N, 121.3530° W

), showcasing their varied geography and economy.

2.4.2. Presentation of the Inter–Exterior Model

We utilized a modified Cobb–Douglas production function to account for technological growth, labor, capital, and climate variables, aiming for a detailed depiction of NWT’s climate dynamics. The equation is:

$$Y_{it}=e^{A_{it}}·L_{it}^{\alpha }·K_{it}^{\beta }{·C_{it}^c·e^{\left({\epsilon }_{it}+v_i\right)}}_{}$$

(1)

where (E) signifies the error component, (t) denotes time, and (i) represents regions. The function encompasses:

• Y_it: GDP or its growth rate at a specific time and region.
• A_it: level of technological advancement.
• K_it: accumulation of physical capital or tangible investments.
• L_it: human capital accumulation, quantified by employment figures.
• C_it: the climate regime, bifurcated into average annual temperature and precipitation metrics.

The adapted Cobb–Douglas production function was used to predict GDP based on labor, capital, and current climate metrics. The model can be simplified as:

$$ln{Y}_{it}=A_{it}+{\alpha lnL}_{it} +{\beta lnK}_{it} {+cln{C}_{it}+\mathcal{E}}_{it}+v_i$$

(2)

This expansion is in line with research from Barro (1991), Levine and Renelt (1992), and Alagidede et al. (2016) [18,19,20].

Our study examined climate change’s effects on NWT’s economic growth using three climate variables. To avoid multicollinearity and to obtain consistent results, various model versions were explored. Both linear and log-linear forms were used, with the latter highlighting the relationships between dependent (Y) and independent variables (L, K, C). Separate models were developed, associating GDP with individual or multiple climate metrics.

Co-integration techniques are primarily used when variables are found to be non-stationary or have a unit root. This is based on the understanding that non-stationary variables can give rise to spurious regressions if not handled appropriately. But, in our study, almost all variables were stationary (Appendix B). Thus, given that most of our variables were stationary at levels, there was not a pressing need to check for co-integration among them. This reduced the need for co-integration testing, ensuring that our analysis avoided the pitfalls of spurious regression and offered robust and reliable results. For the “Ta” variable, which is non-stationary, methods like differencing and the inclusion of appropriate control variables such as precipitation and SPEI were employed to ensure the validity and reliability of the analysis.

2.5. Model Calibration

The CGE model often faces questioning due to perceived weaknesses in its empirical parameter estimations, and constraints stemming from data limitations might compromise the integrity of simulation outcomes [21]. To bolster the reliability of our simulation findings, we undertook rigorous calibration processes, particularly focusing on elasticity and marginal propensity parameters. Calibration, a commonly utilized approach for parameter determination, was implemented in line with standard procedures, anchoring the model to the base year, 2016. Once parameters were set, the model was primed for simulating various policy-driven impacts or “shocks”. The resulting solution yielded a fresh equilibrium that could then be compared with the benchmark equilibrium.

2.6. Policy Simulations

In our study, we initially established a benchmark scenario by simulating the status of economic variables without introducing a carbon tax (business as usual). This provided a baseline against which to measure the impact of various carbon tax scenarios and, subsequently, implementing carbon pricing, with and without a rebate offset, on the GDP and household consumption in the NWT. We also applied the dynamic CGE model, adjusting for climate change influences, to analyze the outcomes based on the Intergovernmental Panel on Climate Change’s (IPCC) established greenhouse gas (GHG) emission scenarios spanning from 2006 to 2100. These IPCC scenarios, known as the representative concentration pathways (RCP), offer three distinct trajectories:

• RCP 2.6 (low): a scenario where CO₂ emissions peak by 2020 and then gradually reduce to zero by 2100.
• RCP 4.5 (moderate): a pathway where CO₂ emissions continue to rise until mid-21st century, eventually plateauing thereafter.
• RCP 8.5 (high): a scenario illustrating unchecked growth, where both CO₂ emissions and population see consistent increases throughout the 21st century, as detailed by Van Vuuren et al., 2011 [22].

In the Canadian context, these scenarios hold significant relevance. Given Canada’s vast expanse, diverse ecosystems, and commitment to environmental sustainability, understanding the economic implications of these different emission trajectories is critical. The country’s industries, resources, and overall economy are intricately linked to environmental health. Thus, simulating the economic implications under various carbon tax scenarios can guide policymakers in Canada towards more informed, effective, and future-proof decisions. Analyzing such scenarios can pave the way for strategies that balance both economic growth and environmental preservation, ensuring a sustainable future for generations to come.

3. Results

3.1. Results of the Inter–Exterior Model

Table 2. Interior–exterior model results.

	Dependent Variable:
	Ln (GDP/Labor)
	Agriculture (1)	Oil and Gas (2)	Construction (3)	Manufacturing (4)
Ln(capital/labor)	−0.310 ***	0.854 ***	0.753 ***	0.686 ***
	(0.032)	(0.117)	(0.016)	(0.073)
Temperature	0.015	−0.01	0.003	0.033
	(0.013)	(0.077)	(0.009)	(0.022)
P20mn	−0.127 ***	0.055	−0.060 *	−0.164 *
	(0.047)	(0.047)	(0.036)	(0.095)
SPEI	−0.078	−0.133 *	−0.092 *	0.291 **
	(0.079)	(0.077)	(0.052)	(0.127)
Constant	1.246 ***	−0.471 ***	2.072 ***	−1.020 ***
	(0.189)	(0.095)	(0.097)	(0.153)
Observations	100	100	100	100
R2	0.705	0.969	0.962	0.539
Adjusted R2	0.692	0.967	0.961	0.52
Residual Std. Error	0.207	0.233	0.16	0.382
F Statistic	56.644 ***	735.803 ***	602.883 ***	27.800 ***

Note: * p < 0.1; ** p < 0.05; *** p < 0.01; standard error in parentheses; P20mm—precipitations; SPEI—standardized precipitation evapotranspiration index.

showcases regression analyses assessing the influence of climate variables on total factor productivity in four key sectors of NWT’s economy: agriculture, oil and gas, construction, and manufacturing, spanning from 1991 to 2021. Notably, the capital/labor ratio exhibited diverse impacts across these sectors. Specifically, while agriculture experienced a negative association, the other three sectors—oil and gas, construction, and manufacturing—reflected positive correlations with the capital/labor ratio. Precipitation, designated as P20mn, prominently affected agricultural productivity and showed lesser but significant impacts on construction and manufacturing.

The observed negative coefficient for the capital/labor ratio (K/L) in agriculture in the NWT (Northwest Territories) aligns with the geographical and climatic context of the region. The NWT is situated within the Arctic zone, which inherently limits its agricultural potential. The literature indicates that regions with such climatic conditions typically have a restricted agricultural window, confined mainly to summer months. Due to these constraints, much of the agricultural activity remains manual and rudimentary.

Elaborating further on the results, temperature, though seemingly relevant to the oil and gas sector, did not achieve statistical significance in any of the examined sectors. This might be attributed to the presence of the SPEI variable, which integrated temperature elements. SPEI itself was indicative in its associations with agriculture, oil and gas, and construction, yet it demonstrated an unexpected but significant relationship with manufacturing. The robustness of the model was particularly evident in the oil and gas and construction sectors, as reflected by the high R², adjusted R², and F-statistic values. However, the model’s applicability was also validated for agriculture and manufacturing, as indicated by their respective significant F-statistics.

3.2. Results of the Policy Simulations

Table 3. Main macroeconomic effects (CAD millions, percentage change) of the carbon pricing in the NWT.

Sectors	BAU	SIM with Carbon Pricing	SIM with Carbon Pricing Rebate	Change without Rebate in		Change with Rebate in
				CAD	%	CAD	%
Real gross domestic product at market price	3458.70	3387.59	3398.38	−71.112	−2.1%	−60.322	−1.7%
Agriculture, forestry, fishing, and hunting	3.19	3.16	3.18	−0.025	−0.8%	−0.005	−0.2%
Mining, quarrying, and oil and gas extraction	501.05	486.30	489.30	−14.752	−2.9%	−11.752	−2.3%
Utilities	45.37	44.87	44.88	−0.508	−1.1%	−0.498	−1.1%
Construction	435.05	432.22	432.52	−2.824	−0.6%	−2.524	−0.6%
Manufacturing	13.45	13.44	13.44	−0.005	0.0%	−0.005	0.0%
Retail and wholesale trade	66.25	65.24	65.30	−1.013	−1.5%	−0.953	−1.4%
Transportation and warehousing	331.80	325.82	325.92	−5.980	−1.8%	−5.880	−1.8%
Information and cultural industries	75.53	75.60	75.70	0.074	0.1%	0.174	0.2%
Finance and insurance, real estate, and rental and leasing	298.37	299.01	299.71	0.648	0.2%	1.348	0.5%
Professional, scientific, and technical services	114.33	112.91	112.99	−1.419	−1.2%	−1.339	−1.2%
Educational services, health care, and social assistance	54.52	54.41	54.47	−0.114	−0.2%	−0.054	−0.1%
Accommodation and food services	75.09	74.82	74.92	−0.277	−0.4%	−0.177	−0.2%
Other services (except public administration)	42.43	41.76	41.79	−0.664	−1.6%	−0.634	−1.5%
Public administration	1059.31	1059.91	1060.00	0.600	0.1%	0.690	0.1%

presents the outcomes of implementing carbon pricing, with and without a rebate offset, in the NWT. As can be observed, implementing carbon pricing without an offset led to several significant economic shifts (Figure 1).

The real gross domestic product (GDP) of the NWT is projected to decrease by an average of 2.1% annually from 2020 to 2040 due to carbon pricing. While most sectors witness a dip in their real value-added contributions, the most affected are mining (−2.9%), transportation (−1.8%), other services barring public administration (−1.6%), and retail and wholesale trade (−1.5%). This translates to foregone annual real GDP of CAD 15 million, CAD 6 million, CAD 664 thousand, and CAD 1 million, respectively, over this period. In contrast, the finance, insurance, real estate, information and cultural industries, and public administration sectors are poised to see slight gains. Additionally, the extent of real GDP losses amplifies over time.

Figure 2 depicts the influence of the carbon pricing policy, without a rebate, on household consumption over the 2020–2040 duration. Despite the anticipated shift in consumer expenditure from more expensive carbon-intensive products to more affordable and eco-friendly alternatives, household consumption is negatively affected.

Conversely, Figure 3 illustrates the potential mitigation of this negative effect when a rebate is provided to households post-carbon pricing execution. The rebate to households alleviates some of the impacts on consumption expenditure.

From the data in Table 3 and the associated figures (Figure 1, Figure 2 and Figure 3), several insights emerge. Primarily, the rebate causes a moderation in the GDP decline to 1.7% over the period. While some sectors exhibit reduced rates of decline, others like utilities, transportation and warehousing, and public administration do not exhibit any significant change whether the carbon pricing is implemented with or without the rebate. A handful of sectors, especially those with high-income elastic demands, depict an output rise.

Introducing the three climate scenarios, the results under the RCP 8.5 scenario are largely positive for GDP. In contrast, RCP scenarios 4.5 and 2.6 are more negative compared with the baseline, likely because of the efforts to curb CO₂ emissions, leading to reduced economic output (Figure 4).

4. Discussion

In the wake of growing environmental concerns, the Canadian government has expressed its commitment to curbing carbon emissions with the institution of a carbon pricing policy to usher in an era where economic agents are incentivized to adopt eco-friendly practices. This study ventured into an in-depth examination of the ramifications of such a policy on the economic landscape of the NWT, probing especially into its performance and structural transformations. Through the integration of climate models, along with three pertinent GHG emission and future warming trajectories, our computable general equilibrium analysis was subjected to climate-related shocks.

The findings from our simulations are illuminating. At the forefront, the introduction of the carbon pricing policy would precipitate a decline in the economic contributions from sectors entrenched in fossil fuel production, inevitably weighing down the GDP and overall economic growth of the NWT. However, an intriguing twist surfaced when households were brought into the rebate system: the economic repercussions of the carbon pricing policy were substantially mitigated. This unveiled a compelling narrative: while the unchecked implementation of a carbon pricing policy can constrict household consumption and overall economic vigor, the introduction of a rebate system can counterbalance these adverse effects, thus presenting policymakers with a nuanced perspective on the delicate equilibrium between economic growth and greenhouse gas emission goals.

Our research stands distinct in the literature on carbon pricing on two notable fronts. Firstly, our approach delved into the distributional aftermath of carbon pricing, navigated through the compass of an intertemporal general equilibrium model, a departure from the traditional static models that often sidestep the nuanced effects of pricing on investments. Secondly, our research pioneered the exploration of the intertwined relationship between growth, equity, and carbon pricing within the unique context of a northern economy graced with an environmentally delicate fabric.

However, as comprehensive as our study strove to be, it was not without limitations. Key hypotheses, especially those orbiting around cap-and-trade mechanisms, were beyond the purview of this study. The parameters evaluated here are but a fraction of the vast spectrum needed for an exhaustive dissection of the carbon pricing policy’s imprint on NWT’s economy. Consider, for instance, the rapidly evolving landscape of electric vehicles, progressively gaining traction owing to their affordability and widening range of options [1]. The carbon footprint of this mode of transportation hinges largely on the environmental efficiency of the electricity generation process. Hence, an in-depth understanding of the interplay between vehicle costs, charging infrastructures, and electricity costs and how they collectively steer the adoption rates of electric vehicles can further refine the policy nuances of carbon taxes.

In summation, while our study shines a light on the multifaceted implications of a carbon pricing policy in NWT, the real-world application of such policies demands a judicious balance. Policymakers in NWT are presented with invaluable insights, and the carbon pricing policy, when judiciously executed, can serve as a formidable tool in the arsenal against escalating greenhouse gas emissions, all the while nurturing the economic prosperity of the region.