How Australia Will Meet Its 2030 Emissions Target—Mapping the Optimal Emissions Pathway

Abstract

Australia submitted its updated nationally determined contribution (NDC) in 2022 and increased the ambition of its 2030 target, committing to reduce greenhouse gas (GHG) emissions 43% below 2005 levels by 2030. A new set of policies was proposed in its NDC, but the potential effectiveness of these policies should be assessed. This research has applied an environmentally extended input–output analysis combined with linear programming and set six types of scenarios to assess the maximum GHG emission reductions in 2030. The six scenarios include “business as usual”, different levels of sector-differentiated growth, a low-carbon electricity mix, a reduction in the emissions intensity of the mining sector, an increase in the electricity efficiency of intermediate inputs, and the implementation of all measures. Results show that implementing all measures simultaneously can achieve Australia’s 2030 emission targets, with total emissions in 2030 being 317.62 Mt CO₂-e, which is a 39.08% reduction compared to the BAU. This study contributes to understanding changes in scenarios for the development of carbon emissions to achieve Australian NDCs.

1. Introduction

The increasing greenhouse gas (GHG) emissions generated by industrial activities have undoubtedly caused global warming, which results in an increase in climate risks (e.g., higher temperatures, more frequent and severe extreme weather events, and natural disasters). These risks have threatened the stability of human and natural systems [1,2]. International climate agreements have been working to reduce GHG emissions to cope with these risks. The overarching goal of the Paris Agreement, a legally binding international treaty on climate change, is to hold “the increase in the global average temperature to well below 2 °C above pre-industrial levels” and pursue efforts “to limit the temperature to increase 1.5 °C above pre-industrial levels” [3]. An Intergovernmental Panel on Climate Change special report published in 2018 highlighted the critical necessity of limiting global warming to 1.5 °C above pre-industrial levels by the end of this century to mitigate the severe impacts of climate change, which include more frequent and intense droughts, heatwaves, and extreme precipitation events [4]. To limit global warming to 1.5 °C, GHG emissions must peak before 2025 at the latest and decline by 43% by 2030. To achieve this climate goal, countries are required to submit their increasingly ambitious nationally determined contributions (NDCs) every five years under the Pairs Agreement, based on their unique energy resource endowment and socio-economic development profile.

Australia has submitted its updated NDC under the Pairs Agreement in 2022 and promised to reduce GHG emissions 43% below 2005 levels by 2030 and achieve net zero by 2050 [5]. Despite its ambitions to reduce emissions, the aim of the reduction in GHG emissions in Australia has not been reached. According to the Climate Change Performance Index (CCPI), due to Australia lacking detailed plans and policies to achieve the target, the country scored low in the GHG emissions (38th), renewable energy (40th), and climate policy categories (50th), with a very low score for energy use (59th) [6]. Australia also set the 82% renewable energy electricity target and the National Electric Vehicle Strategy. Coal dominates Australia’s energy system, particularly electricity generation, which accounts for half of the electricity generated. As the fifth largest hard coal producer in 2021, Australia not only has abundant coal resources but is also developing its reserves for export. There is uncertainty about the future of new coal mines and the specific time of coal-fired power plant closures over the next decade [7]. This fact is not compatible with its 2030 target and 82% renewable energy electricity target. Additionally, in the transport sector, renewable energy plays a significant indirect role in reducing emissions by generating the electricity required to power electric vehicles [8]. Australia, with its substantial potential for renewable energy, should leverage this advantage to ensure the achievement of its renewable electricity goals. Electrification is central to decarbonizing road vehicles and represents the primary option for reducing emissions from transport, particularly road transport. To this end, government interventions will be necessary to increase infrastructure investment and encourage the uptake of battery-powered electric vehicles [8]. However, the country has still not addressed the rising emissions from transport with either updated fuel policies or effective incentives to purchase battery-powered electric vehicles [6]. It seems that the detailed plans and policies in Australia to achieve its NDC is insufficient.

To meet targets in the updated NDC, the Australian government has proposed a substantial and rigorous set of new policies across the economy that focuses on either decarbonization of the electricity grid or the development of low-emission technologies to drive the transition to net zero [5]. These new policies aim to maximize the emissions reduction impact and to minimize the social and economic loss. The 2030 target in Australia’s NDC is an economy-wide reduction commitment, covering all sectors included in Australia’s national inventory. So, are these new policies or measures effective and sufficient? Will these new policies or measures enable Australia to achieve its 2030 target, and what is the gap to the 2030 target? It is the importance of the evaluation of the proposed measures to reduce emissions that has given rise to this study.

Wassily Leontief, the 1973 Nobel Prize winner in economics, developed the input–output (IO) analysis framework [9], and the fundamental purpose of the IO framework is to analyze the interdependence of industries in an economy [10]. The EEIO analysis is a long-established technique based on the IO model and continues to grow in popularity as a method for evaluating the relationship between economic activities and the resulting environmental impacts [11]. The EEIO analysis has also been widely used to analyze GHG emissions [12,13,14,15], resource use (e.g., energy [16], water and land [17,18], material [19]), and various other air pollutants (e.g., sulfur oxide [20] and PM2.5 [21]).

Linear programming (LP) is a useful optimization tool that helps policymakers to rationally utilize resources when faced with complex and conflicting decision objectives. Since the pioneering work of Dorfman et al. [22] has been conducted, a series of studies have combined the LP model with the IO analysis for different purposes, such as projecting coefficients of an inter-industry input–output matrix [23], modifying multipliers in input–output analysis responding to direct restrictions on production [24], and conducting enterprise risk management [25]. To address environmental issues, linear programming has also been coupled with the IO analysis [26], which is a further valuable exploration of the application of the EEIO model. LP-IO allows for the exploration of the reconciliation of divergent objectives; some studies have combined the EEIO models with multi-objective optimization to simultaneously optimize environmental, economic, and social objectives, such as the trade-offs between GHG emissions, gross domestic product (GDP) growth, energy consumption and employment [27], the minimization of GHG emissions with a minimal social and economic loss [28], and the optimization of an intermediate input source and economic growth [29]. In addition, a more extensive assessment of the possibilities for production efficiency and economic and social impacts of newly enacted policies could be conducted using LP-IO. To achieve their NDC, the emission reduction commitments to the international community, many countries have proposed corresponding measures or policies. However, the effectiveness of these measures depends on how they are implemented in practice and is influenced by the intricate interdependencies between sectors. Therefore, evaluating whether environmental measures or policies can balance environmental, economic, and social goals is crucial in the course of moving to a low-carbon economy. Cayamanda et al. [30] conducted a rigorous high-level evaluation to analyze five types of scenarios to identify the minimum possible GHG emissions intensity per unit of GDP and to outline a path for low-carbon economic growth in the Philippines up to the year 2030. Then, building on the research of Cayamanda et al., Nguyen et al. [31] employed an LP-IO model to explore six scenarios aimed at assessing the potential for GHG emission reductions in Vietnam’s economy by 2030, with the consideration of concurrent economic growth. Their findings suggest that the aggregate effect of all the measures across these scenarios nearly aligns with the country’s NDC pledge.

Through the review of the relevant Australian literature, it is worth noting that there is a lack of research using the LP-IO analysis to evaluate environmental policies or strategies in Australia, particularly the strategies proposed to meet the 2030 target. The combination of an extended input–output analysis and multi-objective optimization model has been used to explore the intricate relationship between socioeconomic indicators of the sectors of the Australia economy, and they optimized the configuration of these sectors to minimize the GHG emissions while maximizing economic and employment levels with cuts to consumption levels [28]. Compared to the work of Rojas Sánchez et al., our research takes the targets and a set of new policies with the latest NDC of Australia as the background, and it uses 2021 data to map the optimal emissions reduction pathway to 2030 rather than using 2009 data to draw the 2009 optimization space. In addition, a series of scenarios based on the new policies in the latest NDC is set to simulate the performance and to assess the effectiveness of these policies.

In order to make up for the shortcomings of previous studies and enrich the research on Australia’s 2030 target, this paper uses the LP-IO optimization model to assess the effectiveness of Australia’s reduction measures to meet its 2030 targets and determine how to achieve minimal GHG emissions equivalents given economic growth targets. The analytical framework is employed for the 17-sector input–output model of the Australian economy, scrutinizing its growth trajectories up to 2030. The subsequent sections of this paper are arranged as follows: Section 2 discusses the methodologies of the EEIO analysis and linear programming and presents the six types of scenarios that have been designed. Section 3 examines the results of simulating the six scenarios. Discussions based on the simulation of results are explored in Section 4. Section 5 shows the conclusion and recommendations for future work.

2. Materials and Methods

2.1. Input–Output Model

The basic form of an input–output model is a system of linear equations that describes the relationships among sectors of an economy using technical coefficients, output, and final demand.

Assume that the economy can be categorized into sectors; the output of a sector can be computed as shown in Equation (1) [10]:

$${\displaystyle \sum _j{a}_{ij}{X}_j+y_i=X_i}$$

(1)

where $a_{ij}$ is the technology coefficient measuring fixed relationships between a sector’s output and its inputs (per unit output of sector $j$ requires $a_{ij}$ unit inputs from sector $i$), $y_i$ denotes sector $i’$s final demand, and $X_i$ and $X_j$ represent the total output of sector $i$ and sector $j$, respectively. $a_{ij}$ is the technical coefficient measuring fixed relationships between a sector’s output and input, which can be expressed as shown in Equation (2):

$$a_{ij}={\displaystyle \frac{x_{ij}}{X_j}}$$

(2)

Equation (1) can be rewritten in matrix form as follows in Equation (3):

$$\mathrm{Ax}+\mathrm{y}=\mathrm{x}$$

(3)

where $\mathrm{A}=[a_{ij}]$ is a matrix of technical coefficients, $\mathrm{y}$ is a vector of final demand, and x is a vector of the corresponding outputs.

Equation (3) can be transformed into the following form as Equation (4):

$$\mathrm{x}={(\mathrm{I}-\mathrm{A})}^{-1}\mathrm{y}$$

(4)

where ${(\mathrm{I}-\mathrm{A})}^{-1}$ is known as the Leontief inverse, which shows the dependence of each of the outputs on the values of each of the final demands.

When accounting for pollution generation associated with inter-industry activities, let $\mathrm{b}$ be the vector of direct emission intensities, where each element represents the amounts of emissions produced per unit of corresponding output. Hence, the total emissions associated with a given vector of total outputs can be expressed as shown in Equation (5):

$$\mathrm{E}=\mathrm{bx}$$

(5)

where $\mathrm{E}$ is the vector of emission levels.

2.2. Optimization Model

In this research, the LP-IO framework was devised to minimize the total GHG emissions, considering that Australia continues to maintain economic growth while implementing GHG reduction strategies. For optimization, productivity in varying levels of sectors is permitted. The objective equation describing the minimization of emissions of all sectors can be written as follows in Equation (6):

$$\min E=b_j{x}_j$$

(6)

where $x_j$ is the total output of sector $j$, $b_j$ is the emission intensity of sector $j$, representing the amount of GHGs produced per unit of output, and $b_j{x}_j$ is the total GHG emissions of sector $j$. Equation (6) calculates the aggregate emissions stemming from economic activities, factoring in the direct emission intensities and the total output of various economic sectors. The environmentally extended IO model gives rise to this equation [32], which is utilized to assess the environmental impacts of an economic system [10].

The GDP grows at a reasonable rate each year; the economic constraint can be expressed as Equation (7):

$${\displaystyle \sum _j{x}_{j,t+1}(1-{\displaystyle \sum _i{a}_{ij}})\ge (1+\alpha ){\displaystyle \sum _j{x}_{j,t}(1-{\displaystyle \sum _i{a}_{ij}})}}$$

(7)

where $x_{j,t+1}$ and $x_{j,t}$ represent the total output of sector $j$ in $t+1$ year and $t$ year, respectively. ${\displaystyle \sum _j{x}_{j,t+1}(1-{\displaystyle \sum _i{a}_{ij}})}$ is the sum of value added (GDP) of all sectors in $t+1$ year, and ${\displaystyle \sum _j{x}_{j,t}(1-{\displaystyle \sum _i{a}_{ij}})}$ represents the value added in $t$ year [33]. $\alpha$ is the growth rate of the GDP.

In order to avoid the economy fluctuating dramatically, it is necessary to simulate the change in the economic activities under a normal situation. We should limit the upper bound and lower bound of fluctuations of a sector’s final demand as follows in Equation (8):

$$k_1\le {\displaystyle \frac{y_{j,t+1}}{y_{j,t}}}\le k_2$$

(8)

where $y_{j,t+1}$ and $y_{j,t}$ describe the final demand of sector $j$ in $t+1$ year and $t$ year, respectively. $k_1$ is the lower bound of the change in final demand and $k_2$ is the upper bound. To guarantee that the economy maintains the desired economic growth level, these constraints are implemented for particular scenarios.

Considering the practical significance of the decision variables, all decision variables (total output of each sector) need to satisfy the non-negative constraint in Equation (9):

$$x_j\ge 0$$

(9)

Lastly, in the optimization model, changes in the total output of each sector need to ensure that the equilibrium relationship in the IO table is not broken. Thus, the constraint that ensures the balance of the IO table should follow Equation (1).

2.3. Data Sources

The Australian input–output table during the 2021–2022 financial year was used in this study, and it was retrieved from the Australia Bureau of Statistics [34]. The table contains 115 industry sectors and is formatted in millions of dollars.

Emission data were collected from the National Greenhouse Accounts 2022 [35] under the UNFCC and the Kyoto Protocol and were divided into 7 industry sectors and the residential sector; the 7 industry sectors were further divided into 38 sub-sectors. The unit of emission data is Gigagram CO₂-e.

Based on the Australia and New Zealand Standard Industrial Classification (ANZSIC) standard [36] and according to the emission intensity and the contribution of each sector to the GHG emissions, the IO table has been aggregated into 17 industrial sectors corresponding to the industrial sectors shown in the National Inventory by Economic Sector (excluding the residential sector) using aggregation technology at the industry level [10]. The nomenclature of the 17 sectors and the associated gross output of each sector are shown in

Table 1. Nomenclature of sectors and corresponding gross output.

Sector Code	Sector Name	Gross Output (AUD Million)
1	Agriculture, Forestry, and Fishing	132,904
2	Mining	456,293
3	Food Product, Beverage, and Tobacco Product Manufacturing	123,068
4	Textile, Leather, Clothing, and Footwear Manufacturing	6429
5	Wood, Pulp, Paper, and Printing	32,200
6	Petroleum and Coal Product Manufacturing	17,691
7	Basic Chemical, Polymer, and Rubber Product Manufacturing	52,336
8	Non-Metallic Mineral Product Manufacturing	22,684
9	Metal Manufacturing	110,658
10	Transport and Machinery Equipment Manufacturing	67,904
11	Furniture and Other Manufacturing	9267
12	Electricity Supply	77,762
13	Gas Supply	5541
14	Water Supply and Waste	49,168
15	Construction	541,889
16	Commercial Services	2,351,607
17	Transport, Postal, and Warehousing	223,685

, while the technical coefficient matrix $A$ is shown in

Table 2. Technical coefficients matrix of the Australian economy in 2021.

Sector Code	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1	0.1833	0.0011	0.3563	0.0709	0.0891	0.0004	0.0140	0.0009	0.0002	0.0003	0.0025	0.0002	0.0002	0.0005	0.0017	0.0042	0.0007
2	0.0027	0.0519	0.0072	0.0034	0.0186	0.0104	0.0565	0.1355	0.3791	0.0028	0.0093	0.0550	0.0089	0.0033	0.0126	0.0031	0.0061
3	0.0138	0.0010	0.1087	0.0532	0.0015	0.0016	0.0079	0.0014	0.0007	0.0016	0.0035	0.0005	0.0013	0.0028	0.0012	0.0097	0.0008
4	0.0002	0.0002	0.0004	0.0234	0.0011	0.0001	0.0022	0.0005	0.0003	0.0011	0.0051	0.0001	0.0002	0.0005	0.0019	0.0002	0.0002
5	0.0020	0.0005	0.0127	0.0029	0.0819	0.0010	0.0074	0.0074	0.0024	0.0034	0.0795	0.0009	0.0015	0.0016	0.0223	0.0028	0.0017
6	0.0065	0.0045	0.0006	0.0008	0.0004	0.0008	0.0020	0.0009	0.0071	0.0007	0.0003	0.0014	0.0001	0.0040	0.0025	0.0006	0.0110
7	0.0195	0.0037	0.0057	0.0071	0.0168	0.0083	0.0856	0.0073	0.0025	0.0123	0.0180	0.0011	0.0011	0.0046	0.0137	0.0023	0.0016
8	0.0012	0.0005	0.0041	0.0004	0.0033	0.0003	0.0033	0.0457	0.0038	0.0023	0.0033	0.0010	0.0008	0.0028	0.0303	0.0006	0.0006
9	0.0025	0.0097	0.0048	0.0053	0.0121	0.0078	0.0112	0.0498	0.0774	0.0552	0.0682	0.0030	0.0041	0.0080	0.0410	0.0024	0.0041
10	0.0071	0.0066	0.0034	0.0046	0.0067	0.0043	0.0058	0.0063	0.0059	0.0346	0.0074	0.0044	0.0027	0.0035	0.0071	0.0051	0.0164
11	0.0003	0.0002	0.0006	0.0022	0.0009	0.0002	0.0006	0.0007	0.0004	0.0029	0.0046	0.0001	0.0001	0.0008	0.0034	0.0006	0.0003
12	0.0051	0.0097	0.0067	0.0126	0.0144	0.0095	0.0140	0.0139	0.0340	0.0072	0.0136	0.3615	0.0014	0.0135	0.0012	0.0059	0.0051
13	0.0001	0.0010	0.0009	0.0004	0.0028	0.0024	0.0066	0.0040	0.0026	0.0002	0.0002	0.0053	0.3968	0.0000	0.0000	0.0001	0.0001
14	0.0126	0.0014	0.0056	0.0054	0.0062	0.0044	0.0071	0.0094	0.0039	0.0064	0.0049	0.0071	0.0013	0.0665	0.0078	0.0076	0.0050
15	0.0266	0.0366	0.0038	0.0062	0.0345	0.0100	0.0054	0.0094	0.0038	0.0083	0.0329	0.0435	0.0558	0.0336	0.2967	0.0278	0.0357
16	0.1353	0.1117	0.1284	0.1936	0.1971	0.1362	0.1702	0.1732	0.0802	0.2176	0.1290	0.1734	0.1463	0.3540	0.1622	0.2715	0.2506
17	0.0315	0.0196	0.0542	0.0439	0.0837	0.0262	0.0636	0.0875	0.0443	0.0265	0.0322	0.0098	0.0410	0.0187	0.0245	0.0240	0.1565

.

Table 3. Direct emission intensity vector b in Gg CO₂-e/AUD million.

Sector Code	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
	0.3705	0.2999	0.1136	0.0902	0.0904	0.2919	0.4025	0.9602	0.7145	0.0067	0.0064	5.5983	0.8460	0.6516	0.0571	0.0100	0.2791

shows the direct emission intensity vector b for each sector. Sector 12 (electricity supply) has the highest emission intensity of about 5998.3 tons CO₂-e AUD million, while Sector 11 (furniture and other manufacturing) has the lowest emission intensity of about 6.4 tons CO₂-e AUD million.

2.4. Scenarios

Building on the scenario design methodology of [31], six types of scenarios are developed to analyze the potential for GHG emissions reduction in the Australian economy.

Scenario 1: This scenario is the business-as-usual scenario. According to the economic growth projection from 2015 to 2030, we assume the GDP will on average grow by 3% per annum to 2030, which is about the average annual growth rate from 2000 to 2015 [37]. This scenario presumes a stable technological condition (i.e., constant $\mathrm{A}$ and $\mathrm{b}$). Additionally, the assumption is made that the sector’s final demand growth could range from 2.5% to 3.1% annually over the 9-year modeling period.

Scenario 2: Compared to Scenario 1, this scenario offers a broader growth range, with sectoral final demand growth fluctuating between 0.5% to 4% annually over the modeling period. Such an assumption provides certain flexibility to sectors at differentiated growths, which will lead to changes in the production structure over time and result in the transition to a low-carbon economy by prioritizing the development of low-polluting sectors and limiting the high-polluting sectors. The scenario illustrates the potential to redirect economic growth by implementing diverse measures targeting specific sectors.

Scenario 3: Renewable energy and energy efficiency technologies are emphasized for GHG mitigation in the electricity sector. This scenario, based on Scenario 2, features a 57% decrease in emission intensity of the electricity sector compared to the 2021 level. Such a reduction can be obtained via a substantial and rigorous suite of new policies across the economy in the Australia’s NDC [5]. Under these policies, Australia unlocks a greater penetration of renewable energy and accelerates decarbonization of the electricity grid, changing the status quo of coal-dominated power generation, which is a major source of the GHG emissions.

Scenario 4: This scenario, based on Scenario 2, presumes a 31% decrease in emission intensity in the mining sector by the year 2030 compared to the 2021 level. This reduction is based on the policies such as the Safeguard Mechanism [38], aimed at reducing emissions from Australia’s largest industrial facilities through setting legislated limits, known as the baseline, on the emissions of the large emitter and regulating their performance. Such policies contribute to high-polluting emitters meeting their emission targets through technology updates, and they help strengthen the competitiveness of industries as the world moves to net zero.

Scenario 5: This scenario, based on Scenario 2, encompasses the broad implementation of energy-efficient measures for final consumption and additional energy-saving measures across the economy. We assume that the above measures result in a moderate reduction of 30% in electricity use as intermediate inputs for production activities. Such measures to improve the efficiency of energy use are included in the National Energy Performance Strategy [39].

Scenario 6: this scenario combines all four measures indicated in Scenarios 2–4, which includes differentiated sector growth, the adoption of a low-carbon electricity mix, and a reduction in emissions in high-polluting sectors.

3. Results

The LP-IO model is solved for each of the six scenarios, and the overall results are summarized in

Table 4. Summary of results of modeling scenarios.

	Scenario 1	Scenario 2	Scenario 3	Scenario 4	Scenario 5	Scenario 6
GHG emissions (Mt)	521.35	471.35	374.03	437.57	415.72	317.62
% reduction in emissions	n/a	9.59	28.26	16.07	20.26	39.08
Emission intensity (tons/AUD million)	162.99	147.36	116.93	136.80	129.97	99.30
GDP (AUD million)	3,198,664	3,198,664	3,198,664	3,198,664	3,198,664	3,198,664

. As can be seen from Table 4, based on the assumptions set for 2030 in Scenario 1, through the analysis of Equations (1)–(9), the estimated GHG emissions are 521.35 Mt, and the estimated GDP is AUD 3,198,664 million. The corresponding GHG emission intensity is 162.99 tons of CO₂-e per AUD million of GDP.

Table 5. GHG emissions load of six types of scenarios (Mt CO₂-e).

Sector Code	Sector	Scenario 1	Scenario 2	Scenario 3	Scenario 4	Scenario 5	Scenario 6
1	Agriculture, Forestry, and Fishing	34.76	30.26	30.26	30.26	30.34	30.34
2	Mining	129.02	108.54	108.55	74.74	108.94	75.02
9	Metal Manufacturing	35.35	31.98	31.98	31.98	32.91	32.91
12	Electricity Supply	184.50	169.31	71.96	169.31	111.66	47.46
14	Water Supply and Waste	19.83	18.86	18.86	18.86	19.01	19.01
15	Construction	15.03	15.34	15.33	15.33	15.35	15.35
16	Commercial Services	19.48	20.56	20.58	20.58	20.61	20.63
17	Transport, Postal, and Warehousing	41.16	37.47	37.47	37.47	37.58	37.58
	other sectors	42.24	39.03	39.03	39.03	39.31	39.31

and Figure 1 depict the GHG emissions load across various sectors under different scenarios. As indicated in

Table 6. Final demand results (billions of dollars) and % change compared to BAU scenario for each sector.

	Scenario 1	Scenario 2		Scenario 3		Scenario 4		Scenario 5		Scenario 6
Sector Code	Final Demand	Final Demand	Change	Final Demand	Change	Final Demand	Change	Final Demand	Change	Final Demand	Change
1	61.27	51.31	−16.25%	51.31	−16.25%	51.31	−16.25%	51.31	−16.25%	51.31	−16.25%
2	460.78	378.91	−17.77%	378.91	−17.77%	378.91	−17.77%	378.91	−17.77%	378.91	−17.77%
3	108.51	86.22	−20.54%	86.22	−20.54%	86.22	−20.54%	86.22	−20.54%	86.22	−20.54%
4	5.22	4.44	−14.91%	4.44	−14.91%	4.44	−14.91%	4.44	−14.91%	4.44	−14.91%
5	8.48	7.03	−17.03%	7.03	−17.03%	7.03	−17.03%	7.03	−17.03%	7.03	−17.03%
6	10.80	8.59	−20.54%	8.59	−20.54%	8.59	−20.54%	8.59	−20.54%	8.59	−20.54%
7	33.94	28.43	−16.25%	28.43	−16.25%	28.43	−16.25%	28.43	−16.25%	28.43	−16.25%
8	2.10	1.76	−16.25%	1.76	−16.25%	1.76	−16.25%	1.76	−16.25%	1.76	−16.25%
9	75.63	63.34	−16.25%	63.34	−16.25%	63.34	−16.25%	63.34	−16.25%	63.34	−16.25%
10	52.10	56.34	8.14%	56.34	8.14%	56.34	8.14%	56.34	8.14%	56.34	8.14%
11	6.97	7.54	8.14%	7.54	8.14%	7.54	8.14%	7.54	8.14%	7.54	8.14%
12	26.65	22.32	−16.25%	22.32	−16.25%	22.32	−16.25%	22.32	−16.25%	22.32	−16.25%
13	1.40	1.17	−16.25%	1.17	−16.25%	1.17	−16.25%	1.17	−16.25%	1.17	−16.25%
14	21.50	18.00	−16.25%	18.00	−16.25%	18.00	−16.25%	18.00	−16.25%	18.00	−16.25%
15	366.76	377.80	3.01%	377.80	3.01%	377.80	3.01%	377.80	3.01%	377.80	3.01%
16	1849.40	1999.88	8.14%	1999.88	8.14%	1999.88	8.14%	1999.88	8.14%	1999.88	8.14%
17	107.15	85.59	−20.13%	85.59	−20.13%	85.59	−20.13%	85.59	−20.13%	85.59	−20.13%

and

Table 7. Total output results (billions of dollars) and % change compared to BAU scenario for each sector.

	Scenario 1	Scenario 2		Scenario 3		Scenario 4		Scenario 5		Scenario 6
Sector Code	Total Output	Total Output	Change	Total Output	Change	Total Output	Change	Total Output	Change	Total Output	Change
1	170.67	148.59	−12.94%	148.59	−12.94%	148.59	−12.94%	148.58	−12.94%	148.58	−12.94%
2	581.28	489.00	−15.88%	489.00	−15.88%	489.00	−15.88%	488.90	−15.89%	488.90	−15.89%
3	161.78	138.07	−14.65%	138.07	−14.65%	138.07	−14.65%	138.07	−14.66%	138.07	−14.66%
4	8.21	7.43	−9.50%	7.43	−9.50%	7.43	−9.50%	7.43	−9.51%	7.43	−9.51%
5	42.25	41.04	−2.86%	41.04	−2.86%	41.04	−2.86%	41.03	−2.88%	41.03	−2.88%
6	23.10	20.03	−13.30%	20.03	−13.30%	20.03	−13.30%	20.02	−13.32%	20.02	−13.32%
7	66.63	60.15	−9.72%	60.15	−9.72%	60.15	−9.72%	60.15	−9.73%	60.15	−9.73%
8	29.62	29.54	−0.26%	29.54	−0.26%	29.54	−0.26%	29.54	−0.28%	29.54	−0.28%
9	140.82	127.42	−9.51%	127.42	−9.51%	127.42	−9.51%	127.40	−9.53%	127.40	−9.53%
10	89.07	92.97	4.38%	92.97	4.38%	92.97	4.38%	92.95	4.36%	92.95	4.36%
11	12.17	12.85	5.55%	12.85	5.55%	12.85	5.55%	12.85	5.54%	12.85	5.54%
12	99.33	91.16	−8.23%	91.16	−8.23%	91.16	−8.23%	70.11	−29.42%	70.11	−29.42%
13	7.06	6.30	−10.71%	6.30	−10.71%	6.30	−10.71%	6.29	−10.91%	6.29	−10.91%
14	63.23	60.14	−4.89%	60.14	−4.89%	60.14	−4.89%	60.12	−4.92%	60.12	−4.92%
15	711.37	725.89	2.04%	725.89	2.04%	725.89	2.04%	725.76	2.02%	725.76	2.02%
16	3087.75	3259.65	5.57%	3259.65	5.57%	3259.65	5.57%	3259.20	5.55%	3259.20	5.55%
17	292.97	266.73	−8.96%	266.73	−8.96%	266.73	−8.96%	266.69	−8.97%	266.69	−8.97%

, which detail the contributions of final demand and sectoral total output, it is evident that the total output across all sectors has seen an approximate increase of 30% under the assumptions of Scenario 1. This increase in total output can be attributed to a compounded annual growth rate of 3% sustained over a period of nine years, characterizing Scenario 1 as a proportional expansion of the entire economic framework. Given the absence of any intervening technological advancements or shifts in industrial structure, this uniform increase in output leads to a corresponding increase in greenhouse gas (GHG) emissions. Figure 2a provides a graphical representation of the contributions of the seventeen sectors to both GDP and GHG emissions.

The GDP derived from Scenario 1 is subsequently used in all scenarios to assess the capacity of various measures to diminish GHG emissions while maintaining the same economic growth level. Scenario 2, featuring varied growth rates across economic sectors, is projected to achieve a reduction in GHG emissions to 471.35 Mt CO₂-e by 2030, representing a 9.59% decrease relative to Scenario 1. This reduction is indicative of the scenario’s efficacy in mitigating emissions through targeted sectoral growth strategies. The associated GHG emissions intensity is reduced to 147.36 tons CO₂-e per AUD million, reflecting a more sustainable economic output. The sectoral contributions to this emissions profile are showed in Figure 2b, providing a detailed breakdown of each sector’s impact on the overall emissions landscape. Such reductions are due to sectoral productivity adjustments, i.e., constraining the expansion of sectors with high emission intensity. By applying existing technological capabilities, this accomplishes the anticipated emission reductions without considering potential alterations due to technological progress and changes in energy consumption patterns.

The result presented in Table 7 reveals that a slight expansion in production is observed in Sector 10 (transport and machinery equipment manufacturing), Sector 11 (furniture and other manufacturing), Sector 15 (construction), and Sector 16 (commercial services). In contrast, a reduction in production is noted across other sectors, with the extent of reduction varying among them. Sector 2 (mining) achieves the highest reduction in production, which translates to a 15.88% reduction in total output, followed by Sector 3 (food product, beverage, and tobacco product manufacturing), Sector 6 (petroleum and coal product manufacturing), and Sector 1 (agriculture, forestry, and fishing), resulting in 14.05%, 13.30%, and 12.94% reductions in total output, respectively, in comparison to Scenario 1. It is noteworthy that sectors with higher emission intensities have not experienced a proportional decrease in production. This result underscores the intricate interplay between various sectors and the reconciliation between economic and environmental goals, which significantly contributes to achieving an optimized equilibrium within the economy. Given the complex nature of the economy, sectors are not isolated entities but are interconnected within a complex network, influencing one another through supply and demand dynamics. Economic growth, often driven by technological advancements, policy changes, and market dynamics, can both alter the economic structure and be influenced by sectoral disruptions. Changes in regulatory frameworks and policies can significantly impact specific sectors, either by imposing new constraints or by creating opportunities for expansion. Therefore, in the process of transitioning to a low-carbon economy, ensuring that policies across different sectors are coherent and complementary can help mitigate severe economic fluctuations and support overall economic growth.

The integration of differentiated sectoral growth with the implementation of a low-carbon power generation mix in Scenario 3, coupled with the deployment of low-carbon technologies within the mining sector in Scenario 4, is projected to achieve a more pronounced reduction in GHG emissions, with estimates for the year 2030 reaching 374.03 Mt CO₂-e and 437.57 Mt CO₂-e, respectively. Compared to Scenario 1, this corresponds to reductions of 28.26% and 16.07%, respectively. At the same time, emission intensity dopped to 116.93 tons and 136.80 tons CO₂-e per AUD million, respectively. The sectoral contributions to this emissions profile are shown in Figure 2c,d. The combination of differentiated industry growth with widespread electricity use saving measures reduces GHG emissions for the year 2030 to 415.72 Mt CO₂-e, corresponding to a reduction of 20.26% compared to Scenario 1, and emission intensity achieved 129.97 tons CO₂-e per AUD million. The GDP and GHG emissions performance of each sector in Scenario 5 are shown in Figure 2e.

Finally, the solution for Scenario 6 combines with all of the strategies outlined in Scenarios 2–5, significantly reducing the GHG emissions in 2030 to 317.62 Mt CO₂-e, while the corresponding emissions intensity level is reduced to 99.30 tons per AUD million. Compared to the result of Scenario 1, this measure helps to reduce the GHG emissions by 39.08%. The GDP contributions and GHG emissions attributed to the seventeen sectors are graphically depicted in Figure 2f. Based on these results, Scenario 6 is identified as the optimal strategy for mitigating GHG emissions with a total GHG emissions value of 317.62 Mt CO₂-e in 2030, followed by Scenario 3 at 374.03 Mt CO₂-e, Scenario 5 at 415.72 Mt CO₂-e, Scenario 4 at 437.57 Mt CO₂-e, and finally Scenario 2 at 471.35 Mt CO₂-e. Notably, if only a single strategy is implemented, the emission reductions in Scenario 3 are most significant when reducing the emissions intensity of electricity.

4. Discussion

The analysis of the aforementioned scenarios reveals the potential efficacy of five measures to reduce GHG emissions in the Australian economy. These measures include a wider range of differentiated growths of sectors to change Australia’s economic structure (e.g., away from relatively more GHG-intensive economic activities towards less intensive ones); the decarbonization of the electricity mix through the transition to renewable energy generation; the enhancement of emission reduction technologies in the mining sector; the widespread improvement of electricity use efficiency; and the implementation of all the aforementioned measures. Furthermore, each scenario imposes constraints on economic growth and production to align more closely with realistic conditions and avoid exaggerated economic fluctuations. Results show that permitting a more extensive range in differentiated sector growth leads to a 9.59% decrease in GHG emissions in 2030 compared to the BAU (Scenario 1). Additionally, combining the broader range in differentiated sector growth with the decarbonization of the electricity mix results in a 28.26% reduction in GHG emissions; the enhancement of emission reduction technologies in the mining sector contributes to a 16.07% reduction and the widespread improvement of electricity use efficiency contributes to a 39.08% reduction.

If all strategies are concurrently enacted, a reduction of 39.08% in GHG emissions by 2030 relative to BAU is attainable, reducing the GHG emissions from 521.35 Mt CO₂-e to 317.62 Mt CO₂-e. In light of Australia’s pledge to decrease GHG emissions by 43% compared to 2005 levels by 2030, translating to a reduction from 616 Mt CO₂-e to 351 Mt CO₂-e, the findings indicate that Australia is poised to meet its 2030 target. It is reasonable to assert that Australia is progressing towards a low-carbon economy and that its 2030 goal is consistent with the current trajectory. Nonetheless, achieving this objective necessitates the simultaneous implementation of a diverse array of GHG-mitigating strategies.

Although the simulations of each mitigation measure in this study have certain effects on emissions reduction, the actual effectiveness of each measure depends on the efforts made to implement the policy. It is worth noting that the implementation of climate policies still faces certain challenges. First, the government’s attitude will affect the policy effect to a certain extent, and whether the Australian government can implement coherent climate policies in the long term plays an important role in the realization of emission reduction targets. Second, the innovation of clean technology is an important basis for emissions reduction in each industry, and unless the correct policy incentives are implemented, the contradiction between emissions reduction costs and economic benefits cannot be reconciled. Finally, the transition to renewable energy is not a one-step process, and the transformation of existing energy infrastructure, the development and deployment of new technologies, policy support, and the improvement of public awareness may all be places where challenges occur.

Sectors exerting a significant influence over GHG emissions warrant escalated attention from policymakers. Among these, the electricity sector, crucially linking various industries, has a major role in cutting GHG emissions. A quick decarbonization of the power grid provides a foundation for reducing emissions in other sectors. Renewable energy sources are a crucial component of strategies to mitigate climate change. Despite Australia’s substantial potential for harnessing renewable energy resources, the nation continues to rely heavily on fossil fuels, particularly coal, to meet a significant portion of its energy demands, especially in the domain of electricity generation—where coal accounts for approximately half of the electricity produced. This dependence highlights the need for a shift towards a renewable energy development strategy to reduce coal mining activities and increase the share of renewable energy in the electricity generation mix. Australia has huge potential for renewable energy and should make full use of this potential for wind and solar power, which can be instrumental in decarbonizing the electricity system. However, improvements in the electricity sector alone are insufficient. Emissions reductions in other carbon-intensive sectors and a greater focus on consumption sufficiency are also necessary [40].

Focusing on decarbonizing the electricity sector is crucial. However, high-energy-intensive industries also represent promising markets for the adoption of decarbonizing technologies. The transition to electrification is a suitable option for emissions reduction in the transport sector that exerts a significant and undeniable impact on GHG emissions. According to the Electric Vehicles Council [41], electric vehicle sales in Australia have shown steady growth, with 85,319 units sold by the end of September 2024. EVs now account for 9.5% of all new car sales in Australia, marking a significant increase from 8.4% in 2023. Despite this growth, the market penetration rate remains relatively low. Additionally, Australia’s high dependency on imported fuels has rendered it vulnerable to fluctuations in the global oil market. EVs offer a significant opportunity for Australia to reduce fuel costs while maximizing the utilization of locally produced energy. Under the dual risks of insufficient EV market penetration and unstable oil supply and intermediate and strong policy action [42], large-scale rapid transitions to electric road transport and 100% renewable electricity [43] are necessary. The Australian government should develop nationally consistent policies to increase the supply of EVs into the country and alleviate barriers to electrification across the transport sector. Additionally, the government and transport sector should elucidate to the public the beneficial impacts of transitioning to EVs for low or even zero emissions and provide appropriate incentives to encourage EV purchases.

The mining sector, which is a high-energy-intensive industry, has not received the same level of attention and support as the electricity sector for the implementation of behind-the-meter renewable energy technologies [44]. As the world’s leading producer and exporter of coal, Australia’s immediate priority is to implement effective strategies in its mining sector to reduce GHG emissions. This includes integrating renewable energy sources [44], enhancing research and development of new technologies aimed at improving the energy efficiency of mining equipment and processes, promoting economic diversification in communities that have traditionally relied on coal, and creating jobs in the booming renewable energy sector. Given the significant contribution of the mining industry to Australia’s GDP, it is important that these emission reduction strategies are implemented in a way that ensures a balanced economy and preserves the social well-being of the population.

Given the relatively small proportion of GHG emissions attributed to the agricultural sector in many developed countries, the potential for GHG reduction through agricultural practices is somewhat limited [40,45]. However, Australia presents a distinct case. The agricultural sector is a significant contributor to the nation’s GHG emissions. Over the past two decades, GHG emissions from this sector have shown a marked decrease, dropping from around 35% to approximately 6% [35]. Despite the current relatively limited contribution of the agricultural sector to the nation’s GHG emissions, it is highly dependent on international markets. An increase in demand for Australian agricultural products could potentially lead to a substantial rise in GHG emissions from this sector [46]. Therefore, the agricultural sector should continue to be subject to long-term monitoring in its efforts to reduce greenhouse gas emissions.

Moreover, the manufacturing sector’s potential to cut emissions also demands attention, and advanced technologies are indispensable facilitators. Shifting to a green diet can lead to lower emissions in food manufacturing, trimming high-emission activities and associated food consumption emissions. These measures are critical to forging pathways towards sustainable development and fulfilling climate action obligations under international agreements. In developed countries, the agricultural sector usually makes up about 10% of the national GHG inventory.

By doing so, Australia would move closer to a low-carbon economy, reduce harm to the environment, and ensure future resilience. It is essential that the Australian government continues to focus on and implement strong emission reduction policies in the coming years to achieve emission targets, and Australians also need to gradually change their lifestyle habits to adapt to a low-carbon life.

5. Conclusions

In this study, an LP-IO model was developed to evaluate the efficacy of five GHG emissions reduction measures and the feasibility of achieving Australia’s 2030 target while ensuring stable economic growth. The result indicates that the combination of a wider range in differentiated sector growths, the decarbonization of the electricity mix, the enhancement of emissions reduction technologies in the mining sector, and the widespread improvement of electricity use efficiency can reduce GHG emissions by 39.08% in 2030 compare to the BAU, from 521.35 Mt CO₂-e to 317.62 Mt CO₂-e. This result is also consistent with Australia’s 2030 target of reducing GHG emissions to below 43% of the 2005 levels and assists decision-makers in evaluating the potential impacts of implementing diverse targeted emission reduction strategies.

While the previous literature has also explored the impact of various mitigation measures on GHG emissions, this study sets different scenarios to discuss in depth the efficacy of various measures in reducing emissions and the likelihood of Australia’s 2030 emissions target in the context of a new climate policy and focuses on sectoral analysis. Although the study provides a degree of reference for policymakers in formulating climate policy, it still has three limitations. (1) More comprehensive measures could be assessed through the strict assumptions in the scenario analysis. For example, despite Australia’s first National Electric Vehicle Strategy to reduce emissions and accelerate the uptake of electric vehicles proposed in the updated NDC [5], Australia has still not addressed the rising emissions from transport with either updated fuel policies or effective incentives to purchase battery-powered electric vehicles. Additionally, a large-scale transition to electric vehicles increases emissions associated with the construction of charging infrastructure. After the COVID-19 restrictions were lifted, activity in the transportation sector increased and returned to previous levels. Thus, the potential to reduce emissions of the transport sector is not considered in the scenario setting of this study. (2) Given that this study employs a single-region input–output model focusing on GHG emissions caused by production activities within Australia, emissions embedded in international trade are not considered in this study. (3) Considering the lagged nature of the technical coefficient matrix, the current input–output relationships may be affected by past economic activities. The technical coefficient matrix reflects the input–output relationships between various industrial sectors, but these relationships are not static and change over time. Given the extended time span of this study, the technical coefficient matrix may face lag issues, meaning that the current technical coefficient matrix may not accurately reflect the actual economic relationships.

Future research can improve on the limitations of this study by constructing plausible scenarios for the shift to electric vehicles in the transport sector to model the effects of their uptake and associated charging infrastructure on emissions. It could also extend the model to a multi-regional input–output framework to encompass GHG emissions from international trade, offering a more holistic assessment of mitigation policy impacts. Additionally, employing suitable techniques to mitigate the effects of lagged input–output coefficients and to account for the extended consequences of climate policies would be beneficial. While this work focuses on Australia, the same methodology could readily be extended to other countries, assuming the requisite economic and environmental data are accessible. Lastly, future research can enrich the model by incorporating a broader range of social impacts, such as job creation and social equity, in addition to economic impacts. This would enable a more detailed analysis of environmental policies.