Triggering business responses to climate policy in Australia

Abstract

The ‘Porter hypothesis’ predicts that well-designed environmental regulations will stimulate businesses to innovate to reduce their environmental impact for efficiency reasons. This article analyses the impacts and anticipation effects of Australia’s carbon price on firms’ carbon reduction activities, through survey data on 466 medium-to-large Australian businesses. We build upon the Porter hypothesis by demonstrating that the anticipated impact of regulation may be as important as its implementation in triggering environmental innovation, thus developing the notion of a ‘signal’ effect.

JEL Classification: D22 and 033

Keywords

Carbon pricing climate policy environmental regulation porter hypothesis

1. Introduction

Organisational responses to climate change policy remain an emerging area of research. Studies have analysed the assumptions underpinning government policy aimed at influencing organisational practices (Shove, 2010; Whitmarsh et al., 2011; Wilson and Chatterton, 2011), lobbying fforts of organisations to shape climate policy (Martin and Rice, 2014) and organisations’ justifications for their climate practices (Nyberg and Wright, 2012). However, analysis of responses to carbon pricing at the firm level has been limited, including in Australia, which is the focus of this article and which represents an appropriate case for analysing this issue given recent policy developments.

The notion that carbon policy can motivate businesses to reduce emissions reflects international evidence (Borghesi et al., 2015; Brannlund et al., 2014; Dechezlepretre and Sato, 2014; Lundgren and Marklund, 2015). Predictions by carbon pricing opponents of dire economic consequences correspond with conventional economic theory. In contrast, the ‘Porter hypothesis’ (Porter, 1991; Porter and van der Linde, 1995) predicts that well-designed (especially market-based) regulations will tend to stimulate businesses to innovate and improve efficiency, thereby offsetting compliance costs.

In this article, we analyse the anticipation and effect of carbon policy in Australia, especially the carbon price which operated from 2012 to 2014, on firms’ activities. We examine the progress of industry emissions and situate these within the political context under which they occurred. Through survey data on 466 medium-to-large businesses, we also examine the motivations and nature of organisations’ emission-reduction efforts. We assess whether these innovations were likely to offset carbon pricing costs through greater organisational efficiency.

The article supports and extends the Porter hypothesis by demonstrating that the anticipated impact of regulation may be as important as its implementation. It develops the notion of a ‘signal’ effect, whereby triggers for environmental innovation may arise not only from regulation itself but also from the political climate which makes regulation seem imminent.

2. Environmental regulation and innovation

Overcoming organisational inertia regarding environmental issues may require substantial regulatory stimulus. Regulations may also facilitate opportunities if organisations respond strategically (Kolk and Pinske, 2012; Unruh, 2002). Orthodox neoclassical economic theory generally denies the possibility that regulations can make firms more efficient. However, Porter and van der Linde (1995: 99) reject the ‘Panglossian belief that firms always make optimal choices’. Instead, they argue, environmental regulations can motivate firms to focus on potential inefficiencies in their use of resources, through technological and process innovations that can offset regulatory compliance costs.

Much of the subsequent testing of the ‘Porter hypothesis’ has utilised Jaffe and Palmer’s (1997) typology for its possible interpretations. This includes a ‘narrow’ version, whereby only certain types of environmental regulation (generally market-based and ‘flexible’) are likely to stimulate innovation; a ‘weak’ version, whereby regulations will stimulate certain kinds of innovation; and a ‘strong’ version, in which innovation stimulated by environmental regulation makes businesses more profitable than they would otherwise be.

There is wide empirical support for the weak version of the hypothesis, but less for the strong version. Several studies find that environmental regulation stimulates innovation, but not sufficiently for a net improvement in profitability (Ambec et al., 2013; Dechezlepretre and Sato, 2014; Lanoie et al., 2011). However, Cohen and Tubb’s (2015) meta-analysis finds that flexible regulations improve competitiveness at the national level, supporting both the strong and narrow versions of the hypothesis.

Yet, this typology excessively constricts Porter and van der Linde’s (1995) more nuanced position that ‘properly designed environmental standards can trigger innovation that may partially or more than fully offset the costs of complying with them’ (p. 98; emphasis added). Jaffe and Palmer’s (1997) ‘weak’ version largely excludes the potential for innovations to offset compliance costs, whereas the ‘strong’ version sets a higher standard of improved profitability. Regulations may motivate innovations which do not increase efficiencies sufficiently for a net benefit to profitability, but which do offset compliance costs. If regulation can facilitate environmental improvement with minimal negative impact on profitability because regulation-stimulated efficiencies offset compliance costs, then this is an obvious win for policy makers. Moreover, in their assertion that ‘environmental regulations can actually enhance competitiveness’ (1995: 98), Porter and van der Linde refer to the aggregate level, but also emphasise this at the firm level.

Much of the literature measures regulation-driven innovation through counts of patents and research expenditures (see Ambec et al., 2013). However, this view of innovation is limited. Borghesi et al. (2015) distinguish between ‘technological’ and ‘organisational’ innovations, the latter involving innovation in processes and practices not requiring new technology. This broader view is consistent with the original perspective of Porter and van der Linde (1995), which characterises various product and process improvements – few likely to be patentable inventions – as innovation.

Research linking the ‘Porter hypothesis’ to climate change specifically (rather than environmental issues generally) remains limited. Extant research provides tentative support for aspects of the ‘Porter hypothesis’ regarding carbon pricing. Lundgren and Marklund’s (2015) study of carbon pricing effects on Swedish industry found it stimulated improved environmental performance with a neutral effect on profits. They found that only environmental improvements not attributable to carbon pricing resulted in net profitability increases. This does not support the ‘strong’ version of the hypothesis; however, the net zero profitability impact implies the costs associated with the carbon price were offset by efficiencies stimulated by regulation, as Porter and van der Linde (1995) predicted. Dechezlepretre and Sato’s (2014) review of the literature found that carbon pricing induces innovation in less carbon-intensive technologies, but without net improvement in competitiveness, because innovation is directed to emissions reduction at the expense of other kinds of innovation. Borghesi et al. (2015) found the EU’s emissions trading scheme from 2005 was important in spurring emissions-reducing innovation, but this varied by industry.

Policy uncertainty may affect the degree and nature of innovation, though this varies with the specific policy context. Hoffman et al. (2009) found neutral or positive effects on innovation investment stemming from regulatory uncertainty in Europe. Lopez et al. (2017) found that so-called regulation-induced uncertainty – about effects on prices and markets – encouraged investment in emissions reduction, while uncertainty about the nature of the regulation had no observable effect. These sources contend that firms may seek to leverage competitive resources for potential advantages from innovation under uncertainty.

However, regulatory uncertainty over the degree of policy stringency in Europe – where relatively strong action on climate change has been embedded for some time – substantially differs to the uncertain policy context in Australia. Forward-looking businesses in an uncertain policy context may act in anticipation of major policy changes which may seem likely at a given time.

Australian research has found that uncertainty and discontinuity in climate and energy policy has affected investment decisions in electricity generation. This led to a majority of older, coal-based plants being maintained longer than their designed lifespan. This in turn led to a ‘disorderly’ retirement of older plants since 2013, and a lack of re-investment in equivalent deployable capacity (in, for instance, gas-fired generation), leading to higher energy prices (Nelson, 2018; Simshauser and Tiernan, 2019). Investment in renewables has increased over time, driven initially by a period of strong climate policy and then by increases in energy costs due to the exit of coal-based power, but has not been sufficient to fill the gap left by this exit. In comparison, the United States has had similar relative levels of exit from coal, but much greater levels of renewables investment, driven by substantial state-level climate policy in the absence of national-level policy (Nelson, 2018; Simshauser and Tiernan, 2019). The combination of higher investment and policies such as premium-feed-in tariffs have made renewable energy the lowest cost form of new energy generation, but greater climate policy certainty may be needed to spur sufficient continued investment.

Academic and ‘grey’ literature lend preliminary support to the notion of ‘anticipation’ with respect to climate policy. Haigh (2008) found that Australian energy organisations addressed emissions in anticipation of a carbon price adopted 4 years later (also Australian Industry Group, 2013). Conversely, Teeter and Sandberg’s (2017) study of 19 organisations liable under the Australian carbon price indicated that, during its operation, they generally avoided carbon emissions reduction and often focused on lobbying efforts. This limited engagement was derived partly from regulatory uncertainty associated with the future of the carbon price, but may also be characterised as anticipating a change of government leading to removal of the carbon price (which they sought to facilitate).

3. Methods

We examine the relationship between changing climate policy and carbon emissions of Australian businesses drawing on the Australian Greenhouse Emissions Information System (AGEIS, 2018). AGEIS data enables tracking of emissions generated by major industry sectors, albeit in some cases much larger industry groupings than provided by the Australian and New Zealand Standard Industrial Classification (ANZSIC). We are also able to separate direct emissions from ‘Scope 2’ emissions arising from purchased electricity, enabling assessment of energy efficiency outside the electricity sector.

In this way we measure organisational efforts to reduce emissions indirectly, at an aggregate level. We do not limit our consideration to those organisations directly affected by the proposed or implemented carbon price, but assess the more general changes in behaviour which carbon policy is designed to elicit.

3.1. Survey of Australian businesses

To evaluate engagement with climate change at the organisational level, we surveyed 466 medium to large Australian businesses with 20 or more employees in August 2013. The sample was obtained from business information company, Dun and Bradstreet. The ABS Counts of Australian Businesses, June 2012 was used as a population reference to stratify and weight the sample for business size, sector and state to ensure representativeness. Businesses employing 100–199 and 200+ people were over-sampled. Subsequently, some operationally similar sectors were combined for analysis at the industry level due to small samples. Table 1 shows the distribution of the sample across industry sectors.

Table 1.

Business sample.

ANZSIC division	Unweighted sample size, n	Sample %	Population %
Agriculture, Forestry and Fishing	18	4	5
Mining	5	1	1
Manufacturing	55	12	11
Electricity, Gas, Water and Waste Services	4	1	1
Construction	33	7	9
Wholesale Trade	30	6	7
Retail Trade	56	12	13
Accommodation and Food Services	71	15	16
Transport, Postal and Warehousing	21	5	4
Information Media and Telecommunications	8	2	1
Financial and Insurance Services	16	3	2
Rental, Hiring and Real Estate Services	12	3	4
Professional, Scientific and Technical Services	38	8	9
Administrative and Support Services	38	8	7
Education and Training	14	3	3
Health Care and Social Assistance	29	6	4
Arts and Recreation Services	7	2	2
Other Services	11	2	3
Total	466	100	100

ANZSIC: Australian and New Zealand Standard Industrial Classification.

Survey respondents were managers who were available and able to comment on major strategic issues within the organisation, including carbon reduction strategies. The response rate for the 1757 businesses that were within scope (20 or more employees, suitable manager available, direct answering of phone) was 26.52%.

Respondents were questioned regarding the:

Nature of carbon reduction practices, including reducing energy consumption, reducing waste, increasing recycling and engaging in other activities that reduce or offset carbon emission;

Motivations and triggers to engage in activity that reduces emissions, including government policy, corporate objectives, marketing strategy and requests from stakeholders including customers, suppliers, employees and unions;

Impact of emissions reduction activities on organisational operations and staffing;

Time frame for emissions reduction activities undertaken.

The sample was designed to be representative of Australian medium-large businesses generally, rather than focusing on high emitters. While Australia’s carbon price only applied directly to large emitters, indirect effects from higher energy and other prices along supply chains were designed to reduce other businesses’ carbon footprints. This is also reflected in opposition to the carbon price among businesses to which the carbon price did not apply directly. Moreover, other elements of carbon policy referred to below were less specifically directed at high emitters.

Our survey provides data on self-reported behaviours likely to have the effect of reducing carbon emissions, rather than measuring the actual degree to which organisations reduced their emissions. This is a necessary limitation since only a small minority of all medium-large businesses likely measured their actual emissions as shown in extant scholarship (Birchall et al., 2015; Depoers et al., 2016). The survey also captures a plurality of innovative emissions-reducing behaviours.

We have no basis for assessing the ‘strong’ version of the Porter hypothesis – whether emissions innovations resulted in increased profitability and competitiveness – but we are not wholly restricted to the ‘weak’ version. The nature of a carbon price, such as the one operating in Australia at the time the survey was conducted, is such that emissions reductions will necessarily contribute to offsetting regulatory costs.

Attribution of emissions reducing behaviours to climate policy is assessed in various ways. Organisations were asked their reasons for reducing emissions, including the role of the carbon price and whether cost reduction was a key factor. Because carbon price both increases unit energy prices and can serve to increase awareness of these costs, it is likely that it contributes indirectly to this motivation. We also allowed for anticipated regulation in driving decision-making: respondents were asked when they had begun to reduce emissions either since 2008, being the first full year of the Labour government elected on a policy platform of climate change action, or before 2008. This means of assessing the influence of anticipated regulation was necessarily indirect, since by the time of the survey the apparent unpopularity of the implemented carbon price may have made businesses reluctant to attribute their actions to the carbon price.

Following Borghesi et al. (2015) and Porter and van der Linde (1995), we define ‘innovation’ broadly. Accordingly, questions were asked about the nature of emission-reduction technological or organisational innovations, including whether these involved energy reduction, new technologies, new products and various organisational activities listed in Table 2.

Table 2.

Nature and extent of carbon emissions reduction activities undertaken by organisations.

Total explicitly taken steps to reduce/offset carbon emissions	62
Taken steps to reduce energy consumption explicitly to reduce emissions	53
Taken steps to reduce energy consumption not explicitly to reduce emissions	23
Total taken steps to reduce energy consumption	76
Increased material recycle explicitly to reduce emissions	50
Increased material recycle not explicitly to reduce emissions	28
Total increased recycling	78
Reduced amount of waste produced explicitly to reduce emissions	33
Reduced amount of waste produced not explicitly to reduce emissions	38
Total reduced waste	71
Total reduced energy consumption/increased recycling/reduced waste, either explicitly or not explicitly to reduce emissions	94
Introduced new equipment to cut emissions	40
Introduced new technology to cut emissions	28
Introduced new work practices to cut emissions	39
Had staff training or skills development to cut emissions	24
Changed the mix of skills in your workforce to reduce emissions	9
Changed the formulation of any products or services to cut emissions	16
Changed the physical packaging of any products to cut emissions	12
Made changes to your supply chain to cut emissions	19
Bought carbon credits to offset carbon emissions	4
Other	12
Not explicitly taken steps to help reduce or offset carbon emissions	33
Don’t know if done things to help reduce or offset carbon emissions	5
Total	100
Sample n	466

By creating a ‘price signal’, a carbon price may also stimulate action to reduce emissions, including energy consumption, even when organisations do not link these activities to emissions reduction. Some organisations undertake behaviours which are likely to effect carbon emissions reduction, but do not necessarily associate these behaviours with steps to ‘reduce carbon emissions’ per se. We attempted to capture this through a two-step process.

First, respondents were asked if, in recent years, their organisation had done anything to reduce or offset its carbon emissions and, if so, which predefined measures they had undertaken. These included reducing energy consumption, reducing waste and increasing recycling, along with several other possible actions.

Second, a follow-up question determined if organisations had taken steps to reduce energy consumption, reduce waste material or increase recycling for reasons not connected with reducing carbon emissions per se. This was asked of those who reported not taking steps to reduce emissions and those who had but did not report energy, waste or recycling behaviours as part of their emission reduction practices.

This two-step approach allowed us to capture a complete picture of the number of organisations explicitly claiming to have taken steps to reduce their carbon emissions, the measures adopted to achieve this, and the number of organisations adopting behaviours connected with reducing energy consumption, reducing waste material and increasing recycling, regardless of whether these behaviours were perceived to be carbon emission reduction measures. However, more detailed analysis was undertaken only of those who had explicitly taken steps to reduce emissions.

3.2. Econometric analysis

Utilising our survey data, we analyse the role of various potential motivations for emissions reduction behaviour on the likelihood of firms undertaking carbon-mitigating activities. The subset of explanatory variables capturing these motivations was based upon their relative widespread use, ultimately consisting of reducing costs, carbon price client requirements, corporate social responsibility objectives, marketing strategy, and employee or union requests. In addition to these explanatory variables, we controlled for industry-specific effects (although these are not reported separately in the estimation output). All explanatory variables capture categorical data and are included in dummy variable format.

We utilise binary logistic modelling to quantify the marginal effects of our explanatory variables on individual firm carbon-mitigating activities and innovations. The binary logistic method allows estimation of effects of our explanatory variables on the probability of the firm undertaking any emission-reducing behaviour. Estimating our dependent variable as a probability is dictated by the categorical nature of the data, being a yes/no response to the survey question of whether the firm undertook particular actions to reduce carbon emissions. Ordinary least squares (OLS) estimation of such models can potentially lead to predictions outside of the zero to one probability range. However, the binary logistic estimation method specifically confines the model prediction to be within this defined probability range.¹

Separate models are estimated for energy reduction, recycling and waste. We then specifically examine the technological innovations of introducing new equipment and technology, and the organisational innovations of introducing new work practices and training. We also address the issue of the anticipation of climate policy change in a further model using a dependent variable that reflects whether the firm commenced its emission reduction activities since 2008. The specification of our models is depicted below, with estimation results in Tables 3 and 4

\begin{array}{l} y_{i} = β_{0} + β_{1} R e d u c e C o s t s_{i} + β_{2} C a r b o n P r i c e_{i} + β_{3} C l i e n t R e q u i r e m e n t s_{i} + β_{4} C S R P o l i c y_{i} \\ + β_{5} M a r k e t i n g S t r a t e g y_{i} + β_{6} E m p l o y e e R e q u e s t_{i} + β_{7} I n d u s t r y_{i} + ε_{i} \end{array}

where y_i = carbon-mitigating activity, firm i.

Table 3.

Econometric results for anticipation, energy consumption, recycling and waste reduction models.

	Action since 2008	Reduce energy consumption	Increased recycling	Reduced waste
Constant/base case	0.09***	0.75***	0.56***	0.58***
Carbon price	+0.24***	+0.08***	+0.19***	+0.19***
Reduced cost	+0.51***	+0.16***	+0.02***	+0.04*
Client requirements	−0.05***	−0.02**	+0.21***	+0.04***
Corporate social responsibility	+0.36***	+0.18***	+0.25***	+0.19***
Marketing strategy	−0.05***	+0.16***	+0.26***	+0.18***
Employee request	+0.01***	−0.21***	+0.32***	+0.04***
Nagelkerke R²	0.574	0.352	0.276	0.181
Correct predictions (%)	82.6	80.1	79.3	74.5

Statistically significant at α = 0.10.

Statistically significant at α = 0.05.

***

Statistically significant at α = 0.01.

Table 4.

Econometric results for technological and organisational innovation models.

	Introduced new equipment	Introduced new technology	Introduced new work practices	Staff training/skills development
Constant/base case	0.06***	0.03***	0.05***	0.04***
Carbon price	+0.21***	+0.02***	+0.10***	+0.04***
Reduced cost	+0.22***	+0.07***	+0.21***	+0.04***
Client requirements	−0.01***	−0.01***	+0.01***	+0.01***
Corporate social responsibility	+0.16***	+0.05***	+0.07***	+0.08***
Marketing strategy	0.00**	0.00***	+0.07***	+0.07***
Employee request	+0.05***	+0.07***	+0.08***	+0.01***
Nagelkerke R²	0.525	0.333	0.526	0.398
Correct predictions (%)	77.8	72.8	77.4	81.7

Statistically significant at α = 0.05.

***

Statistically significant at α = 0.01.

We estimate separate models for high- and low-polluting industries in subsequent analysis presented in Table 5, the former consisting of the agriculture, forestry and fishing; mining; manufacturing; and electricity, gas and water sectors. Other industries were included in the low-polluting group.

Table 5.

Econometric model results for high versus low emission sectors.

Polluter industry	Action since 2008		Reduce energy consumption		Increased recycling		Reduced waste		Technological innovations		Organisational innovations
Polluter industry	High	Low	High	Low	High	Low	High	Low	High	Low	High	Low
Constant/base case	0.05***	0.12***	0.58***	0.54***	0.33***	0.66***	0.30***	0.63***	0.05***	0.08***	0.41***	0.09***
Reduced cost	+0.13***	+0.52***	+0.31***	+0.30***	0.00	–0.04***	+0.33***	–0.11***	+0.18***	+0.27***	+0.42***	+0.31***
Carbon price/tax	+0.70***	+0.21***	+0.34***	+0.07***	+0.58***	+0.04***	+0.66***	+0.06***	+0.42***	+0.23***	+0.58***	0.00
Client requirements	–0.03***	–0.06***	–0.24***	–0.07***	+0.25***	+0.16***	+0.14***	–0.03***	+0.22***	0.03***	+0.04**	+0.04***
Corporate social responsibility	+0.37***	+0.34***	+0.13***	+0.29***	+0.31***	+0.21***	–0.15***	+0.23***	+0.08***	+0.27***	+0.14***	+0.16***
Marketing strategy	+0.10***	–0.07***	+0.36***	+0.25***	+0.53***	+0.19***	+0.51***	+0.12***	+0.02***	+0.06***	+0.48***	+0.10***
Employee request	+0.14***	+0.01**	+0.10***	–0.25***	0.00	+0.23***	–0.01	+0.13***	+0.14***	+0.05***	+0.11***	+0.10***
Nagelkerke R²	0.683	0.528	0.385	0.293	0.564	0.201	0.566	0.127	0.675	0.561	0.793	0.529
Correct predictions (%)	92.9	80.3	83.4	75.0	86.8	78.3	80.3	72.2	83.9	78.2	87.0	78.3

Statistically significant at α = 0.05.

***

Statistically significant at α = 0.01.

Model diagnostics for the binary logistic models consist of the Nagelkerke R² as well as the percentage of correct predictions. The transformed marginal effects of the explanatory variables upon the dependent variable probability are reported for the binary logistic models rather than the β coefficients, being of greater interpretive value.

4. Australian greenhouse gas emissions and climate policy, 2005–2016

Figures 1 to 3 show the behaviour of industry-generated emissions in tonnes of carbon dioxide equivalent for financial years ending in June over 2005–2016. Figure 1 shows total direct industry emissions and total industry Scope 2 emissions from purchased electricity.² Industry breakdowns are provided in Figure 2 for direct emissions and Figure 3 for Scope 2. The industries are based on ANZSIC industry classification codes, except the AGEIS combines several service-related sectors in a single category, ‘Commercial Services’.³ Total direct industry emissions briefly plateaued for FY2005–2007, before declining to 2014, steeply from 2010. Scope 2 emissions maintained a continual increase over 2005–2009, before declining.

Figure 1.

Total direct and scope 2 emissions from industry, gigatonnes of CO₂ equivalent, FY2005–2016.

Figure 2.

Direct emissions by economic sector, gigatonnes of CO₂ equivalent, FY2005–2016.

Figure 3.

Scope 2 emissions by economic sector, gigatonnes of CO₂ equivalent, FY2005–2016.

Table 6 summarises the political context of Australian climate policy development for 1998–2009. Following the Howard Coalition government’s refusal to ratify the Kyoto accords, limited progress was made in federal policy pre-2007. A modest renewable energy target (RET) of 10% was implemented in 2001 (Talberg et al., 2016). Legislation in 2006 requiring companies to report on emissions, energy consumption and abatement activities had limited impact (Kumarasiri and Jubb, 2016). Some state-based schemes achieved only modest success (Nelson et al., 2010).

Table 6.

Timeline of Australian climate policy, 1998–2009.

Date	Event
1998 April	Australia signs, but does not ratify, the Kyoto Protocol.
2001 April	Mandatory Renewable Energy Target (MRET) scheme aims for 10% renewable energy.
2006 April	Energy Efficiency Opportunities (EEO) Act passed, requiring high energy usage businesses to investigate and report on (though not implement) opportunities for improving energy efficiency.
2006 December	Prime Minister’s Task Group on Emissions Trading (PMTGET) established by then Coalition (Liberal/National) Howard government.
2007 April	Federal Labor Opposition leader Kevin Rudd with State and Territory governments commission climate policy review by economist Ross Garnaut.
2007 June	Prime Minister John Howard adopts policy to implement carbon trading scheme in 2012.
2007 September	National Greenhouse Energy Reporting (NGER) Act requires reporting on energy usage and emissions by organisations meeting a particular threshold for energy consumption or greenhouse gas (GHG) emissions.
2007 December	Rudd Labour government ratifies Kyoto Protocol.
2008 September	Final Garnaut Review released.
2009 May	After stakeholder consultation, Labour government tables Carbon Pollution Reduction Scheme (CPRS).
2009 August	The CPRS bill defeated in the Senate, opposed by Coalition Opposition and Greens.
2009 September	Renewable Energy (Electricity) Amendment Bill mandates 20% of electricity from renewable sources by 2020.
2009 December	Tony Abbott replaces Malcolm Turnbull as Opposition leader and revised CPRS defeated in Senate. Copenhagen UN Framework Convention on Climate Change summit fails to reach binding agreement.
2010 April	Prime Minister Rudd announces that implementation of an Emissions Trading Scheme (ETS) will be delayed until at least 2012.
2010 June	Rudd replaced by Julia Gillard as Labour leader and Prime Minister.
2010 August	Labour forms minority government with support of independents and Greens.
2010 September	Labour government announces Multi-Party Climate Change Committee (MPCCC) to investigate climate policy.
2010 November	Gillard Labour government commissions an update of 2008 Garnaut Review.
2011 March	The Coalition Opposition starts to maintain consistent lead in most polls.
2011 July	Gillard Labour government releases its carbon reduction policy.
2011 November	Labour’s carbon reduction policy passed in Clean Energy Act.
2012 June	Clean Energy Finance Corporation (CEFC), with US$10 billion to invest in clean energy, legislated.
2012 July	Carbon price under Clean Energy Act begins; US$23 per tonne until July 2015, when to become floating price. Labour government establishes Climate Authority to provide advice on climate change and Renewable Energy Agency, to fund renewable energy technology.
2013 June	Kevin Rudd replaces Gillard as Prime Minister.
2013 July	Rudd announces carbon price will move to an ETS with floating price a year early in 2014; Government report on carbon price finds that energy emissions declined, and increased renewable energy investment
2013 September	Coalition wins 2013 federal election, with Tony Abbott as Prime Minister, quickly begins drafting repeal of Clean Energy Act 2011; and dismantles Climate Commission.
2013 November	Coalition government introduces legislation to repeal carbon price and dismantle Climate Change Authority and CEFC.
2014 July	Carbon price mechanism finally repealed, but Climate Change Authority and CEFC remain intact.
2014 November	Coalition’s Direct Action plan legislated through the Carbon Farming Initiative Amendment Act 2014.

Climate change mitigation policy expanded from 2007, when both the Coalition government and the Labour opposition advocated carbon pricing during the federal election. The Labour government elected in November 2007 ratified the Kyoto protocol, undertook substantial ‘stimulus’ spending directed at reducing household emissions and improving energy efficiency in commercial buildings, which was designed to simultaneously address the economic crisis, and funded research and development (Crowley, 2017; Spies-Butcher and Stillwell, 2009). In September 2009, the RET was increased to 20% by 2020.

Carbon pricing became central to the Labour government’s policy, influenced by an independent review (Garnaut, 2008). After consultation, the government gained the support of key business groups, including the Australian Industry Group (Ai Group) and Business Council of Australia, and by November 2009 a nominal agreement with the Coalition opposition. From the second half of 2009, business could be relatively confident that a carbon price would soon be implemented.

In this context, 2007–2009 was a turning point in Australia’s greenhouse gas (GHG) emissions, which rapidly declined, first for direct emissions, then for Scope 2 emissions. Electricity demand stabilised, after consistently increasing up to this point, while electricity emissions intensity which had climbed between 2005/2006 and 2007/2008 subsequently declined rapidly (O’Gorman and Jotzo, 2014). Total emissions from electricity, which had also been consistently increasing previously, also peaked in 2008/2009, after prices peaked around 2007/2008 in most states before declining (Australian Energy Market Operator, 2018).

The extent of emissions reduction varied between industries, however, as shown in Figures 2 and 3. There was a stark direct emissions reduction (Figure 2) in the electricity and utilities sector, which responded with investment in renewables and to a lesser extent manufacturing. Agriculture, forestry and fishing continued a previous rapid decline. The response for direct emissions for other industries was less clear, although direct emissions are less important for commercial services. Figure 3 shows a clearer delayed response from 2010 in commercial services and manufacturing for Scope 2 emissions. However, mining, construction and transport had little or no response with stagnant or increasing emissions.

From 2010 the momentum for a carbon price waned. The United Nations Framework on Climate Change (UNFCC) summit in Copenhagen in December 2009 failed to create a binding international framework for addressing change. Tony Abbott replaced Malcolm Turnbull as Coalition leader and then opposed the legislation, together with the Greens, who held out for a more stringent scheme, ensuring its defeat in the Senate.

Uncertainty continued through 2011 in an unstable parliamentary situation. Labour’s new climate policy released in July now faced a concerted negative campaign by business groups and the Opposition. Business groups opposed the carbon price level set for the fixed price period from 2012 to 2015. In spite of waning public support, the Labour government’s Clean Energy Act was passed in November 2011. The price was levied directly on 350 of Australia’s largest carbon emitting organisations, responsible for roughly 60% of emissions, though indirect effects would be felt through passing on price increases (O’Gorman and Jotzo, 2014). In 2011–2012, the Gillard Labour government also established a substantial apparatus for the promotion and administration of carbon emissions reduction (Crowley, 2017).

Scope 2 emissions rose slightly in all sectors in FY2012, prior to the carbon price implementation in July 2012, in the context of political uncertainty. However, emissions reduction overall occurred during the period of the carbon price. While electricity prices were also increasing due to other factors during this period, O’Gorman and Jotzo (2014) estimate that 60% of the increase was due to the carbon price. They attribute the decline in emissions from electricity both to shifts in demand and the supply mix, both affected by the carbon price.

The Coalition defeated Labour at the September 2013 election, after 2 years of poor polling for Labour. The Coalition’s pledge to repeal the unpopular ‘carbon tax’ became the key issue in its election campaign, which coincided with when our survey was conducted (August 2013). The new government repealed the carbon price in July 2014 (Crowley, 2017) replacing it with a ‘direct action plan’ using an emissions reduction fund providing incentives for businesses to reduce carbon emissions. Senate resistance limited the Abbott government’s attempt to dismantle much of the statutory climate-related apparatus established under the Gillard government, although the RET was reduced. A reversal of previous trends for reductions in emissions followed, as upswings began from 2014, although both manufacturing and agriculture, forestry and fishing continued their downwards trajectories.

The carbon emissions changes over the period in question cannot be wholly accounted for by changes in economic conditions, including the global financial crisis (GFC). Figure 4 plots Australia’s gross domestic product (GDP) growth rate against that of GHG emissions from industry, from 2002 to 2016. From 2002 to 2007 during the Howard Coalition government, annual GDP growth averaged 3.4%, while industry emissions growth averaged 1.5%. From 2008 to 2013, the period of Labour government, annual GDP growth averaged 2.6%, while industry emissions shrunk by an average of 3.1% per year; signifying a decoupling between emissions and economic growth. However, from 2014 to 2016, following the abolition of the carbon price, GDP growth averaged 2.6%, while industry emissions growth averaged 0.7%, representing a re-convergence of these measures.

Figure 4.

Australian GDP growth rate and growth of greenhouse gas emissions from industry, 2000–2016, per cent.

However, the aggregate pattern of emissions closely correlates with the political context for this period. We cannot link these two patterns causally. However, policy shifts and periods of certainty or uncertainty beforehand broadly coincided with shifts upwards or downwards in emissions, sometimes with a lag to account for impact of investment. The only way we can provide more certainty on these links is by focusing on the firm level, which is the purpose of the survey analysed in the next section.

5. Survey results

Descriptive statistics for nature of carbon emissions reduction activities undertaken by organisations and their motivations are shown in Tables 2 and 7, respectively. Overall, 62% of managers stated that their organisations had undertaken measures to reduce emissions: 53% reduced energy consumption, 50% increased recycling activities and 33% reduced waste. These activities would necessarily increase the efficiency of the firms in question. Energy consumption reductions in particular would also have served to offset carbon price compliance costs, either due to direct regulatory impact or from follow-on costs from energy providers and other businesses further up the supply chain.

Table 7.

Selected motivations/triggers for reducing emissions, among businesses that explicitly engaged in emission reduction activities, by industry.

Column %	Reduce costs	Carbon price/tax	Requirements of commercial clients	CSR policy objectives	Marketing strategy	Employee requests
Agriculture, Forestry and Fishing + Mining	98	45	43	65	39	6
Manufacturing + Electricity, Gas, Water and Waste Services	80	34	51	82	50	24
Construction	74	28	57	59	31	16
Wholesale Trade	63	39	57	72	65	0
Retail Trade	90	38	20	47	35	32
Accommodation and Food Services	95	38	31	66	42	7
Transport, Postal and Warehousing	40	6	18	80	29	6
Info. Media & Telecomms + Financial/Insurance Services + Rental/Hiring/Real Estate Services	83	11	16	70	50	28
Professional, Scientific and Technical Services	50	3	34	86	38	21
Administrative and Support Services	75	9	20	86	42	35
Education and Training	100	37	24	82	73	46
Health Care and Social Assistance	96	8	3	54	20	23
Arts and Recreation Services + Other Services	79	23	16	78	46	4
Total	82	27	32	69	42	20

CSR: corporate social responsibility.

Econometric model estimates for the energy, recycling, waste and anticipation models are presented in Table 3. Those for the technological and organisational innovation models (per Borghesi et al., 2015) are shown in Table 4. Model diagnostics suggest we are correctly predicting the behaviour of 70%–80% of our sample of firms. Significantly, the carbon price and cost reduction triggers tend to dominate other motivations in influencing changes to firm behaviour, displaying positive and statistically significant effects on the probabilities of all emission reduction activities and innovations analysed. However, the magnitude of these influences depends on the specific firm behaviour.

Table 7 shows the motivations for emission reduction cited by at least 20% of emission reducing businesses. Only 27% of emissions reducing businesses (around 17% of total sample) cited Australia’s carbon price as a driver for reducing carbon emissions; though this was as high as 45% of businesses in the high-carbon primary sector, and 34% in manufacturing and utilities. Reducing costs was by far the most common reason for reducing emissions, cited by 82% of businesses which expressly reduced emissions (53% of total sample), while 69% of emissions-reducers cited corporate social responsibility policies, 40% were motivated by marketing strategy, 32% cited client requirements and 20% responded to employee requests.

Using these motivations as our explanatory variables in binary logistic models, our econometric analyses presented in Tables 3 and 4 show that cost considerations had a larger impact than the carbon price on the probability of firms reducing energy consumption by 8 percentage points. However, the opposite was true for recycling and waste management, with the influence of the carbon price on the probability of a firm increasing recycling, or decreasing waste, outweighing that of cost by approximately 15 percentage points.

Considering technological innovations, Table 2 shows that 40% of organisations introduced new equipment and 28% introduced new technology. Econometric estimates in Table 4 indicate that both carbon price and cost reduction considerations increased the probability of firms introducing new equipment by around 20 percentage points. In comparison, observable effects of the carbon price on the introduction of new technology were somewhat lower.

With respect to organisational innovations, Table 2 shows that 39% of organisations introduced new work practices, 24% undertook staff training and 9% changed the skills mix in their workforce in connection to cutting emissions. The econometric estimates of Table 4 show that the carbon price increased the probability of firms introducing organisational innovations in the form of new work practices by 10 percentage points and skills development and training by 4 percentage points. In comparison, cost reduction considerations contribute to an increase in the probability of introducing new work practices by 21 percentage points and training by 4 percentage points.

It is likely that these figures, if taken at face value, understate the importance of regulation in driving these emissions behaviours. This is because the effects of anticipating carbon pricing may have caused organisations to act before the carbon price was implemented in 2012. Managers were also asked when their businesses reduced emissions, and nearly 80% indicated they had taken steps within the last 5 years, since the first full year of the Rudd Labour government in 2008, with one-in-five starting previously. Our econometric analysis (first column Table 3) also suggests the carbon price triggered an increase in the probability of firms taking steps to reduce carbon emissions post 2008 by 24 percentage points. This corresponds with the previously discussed aggregate evidence that emissions fell prior to the carbon price.

Conversely, by August 2013, when this survey was conducted, the repeal of the carbon price seemed likely, given anticipated outcome of the looming election in September 2013, which was won by the Coalition pledging to repeal the ‘carbon tax’. This may have limited the effect of the carbon price. An Ai Group study of its membership in 2013 (Australian Industry Group, 2013) found that two of the most important reasons that businesses had not reduced carbon emissions in response to the carbon price were that they had previously acted in response to anticipated carbon pricing, and second that they (accurately) predicted carbon constraints relaxing in the future.

This supports the notion that a price mechanism, specifically increasing costs associated with carbon emissions, can be a key driver of emission reduction efforts. By increasing energy prices, it seems likely that carbon pricing induced businesses to reduce relevant costs. It is difficult to differentiate the endogenous motivation of business to reduce costs per se, from the extent to which attempts to reduce costs have been driven by regulation. However, the proportion of businesses acting after 2008 – correlated with the pattern of aggregate emissions – gives credence to the latter explanation. Our econometric analysis shows that those firms citing cost considerations as a motivation were 40% more likely to have acted post-2008. This suggests the anticipated effects of carbon regulation intensified concerns about carbon-related costs. This indirect effect is particularly important given many of the businesses were not directly subject to obligations under the carbon price scheme, but would have faced increased energy prices as a result of the carbon price.

Carbon prices do not need to create awareness about emissions to have an effect: they are designed to create a price signal by increasing the unit costs of carbon-emitting activities (such as energy usage) and thereby drive reductions in these activities. Our finding that 31% of respondents claimed not to have taken steps to reduce emissions, but upon subsequent questioning were shown to have undertaken behaviours which would have the effect of reducing emissions, is therefore significant. Most importantly, 23% of respondents took steps to reduce energy consumption, but did not link this to emissions reduction (in addition to 53% who explicitly had). While other factors have also increased energy prices in recent years (O’Gorman and Jotzo, 2014), it is likely that carbon pricing influenced these businesses to reduce emissions despite not consciously doing so.

Undertaking climate change mitigation activities had minimal reported impact on employment: 92% of businesses which explicitly sought to reduce carbon emissions saw no effect on employee numbers, 3% increased their employee numbers and 4% reduced employee numbers due to emissions reduction activities.

As Tables 5, 8 and 9 show, the proportion of organisations undertaking carbon reduction activities varied by industry. Although not an emissions-intensive industry, the education and training sector had by far the highest rate of acting directly to reduce emissions (92%) and had among the highest incidence of engagement with a range of specific emissions reduction activities. The relatively high incidence of engagement with employees through consultative mechanisms and unions is important in explaining the high level of engagement in certain industries, such as education and training (Markey et al., 2016). Among the more carbon-intensive industries, the combined primary industries sector (agriculture, forestry and fishing and mining) and the combined manufacturing and utilities sector had slightly higher than average levels of deliberately reducing emissions, but are comparable to, or exceeded by organisations in less-intensive services industries on this measure, as shown in Table 8. However, Table 9 shows that these two broad sectors were particularly likely to have introduced new equipment, new technology and new work practices.

Table 8.

Incidence of carbon emissions reduction activities, by selected industry grouping.

Industry %	Taken steps to reduce energy consumption explicitly to reduce emissions	Increased material recycle explicitly to reduce emissions	Reduced amount of waste produced explicitly to reduce emissions	Total taken steps to reduce/offset emissions	Sample, n
Agriculture, Forestry and Fishing + Mining	65	51	39	70	23
Manufacturing + Electricity, Gas, Water and Waste Services	61	63	32	71	59
Construction	36	51	27	57	33
Wholesale Trade	27	27	12	35	30
Retail Trade	52	48	23	66	56
Accommodation and Food Services	66	52	36	66	71
Transport, Postal and Warehousing	45	24	35	46	21
Info. Media & Telecomms + Financial/Insurance Services + Rental/Hiring/Real Estate Services	73	63	54	77	36
Professional, Scientific and Technical Services	37	45	27	53	38
Administrative and Support Services	36	49	42	51	38
Education and Training	82	66	66	92	14
Health Care and Social Assistance	51	53	53	68	29
Arts and Recreation Services + Other Services	64	42	20	64	18
Total	53	50	33	62	466

Table 9.

Incidence of activities specifically to reduce carbon emissions, % within selected industry grouping.

	Changed formulation of any products or services	Changed physical packaging of any products	Made changes to supply chain	Introduced new equipment	Introduced new technology	Introduced new work practices	Total taken steps to reduce/offset carbon emissions	Total	Row, n
Agriculture, Forestry and Fishing + Mining	1	9	19	48	41	47	70	100	23
Manufacturing + Electricity, Gas, Water and Waste Services	15	29	26	45	33	49	71	100	59
Construction	8	5	6	36	26	28	57	100	33
Wholesale Trade	18	5	6	25	10	26	35	100	30
Retail Trade	8	7	21	43	34	30	66	100	56
Accommodation and Food Services	31	17	26	48	23	49	66	100	71
Transport, Postal and Warehousing	22	21	27	30	22	39	46	100	21
Info. Media & Telecomms + Financial/Insurance Services + Rental/Hiring/Real Estate Services	4	9	26	66	46	45	77	100	36
Professional, Scientific and Technical Services	23	0	7	28	18	39	53	100	38
Administrative and Support Services	11	7	23	18	27	31	51	100	38
Education and Training	40	22	27	46	70	68	92	100	14
Health Care and Social Assistance	14	3	18	13	14	52	68	100	29
Arts and Recreation Services + Other Services	5	15	1	60	15	18	64	100	18
Total	15	12	18	40	28	39	62	100	466

There is some evidence of complementarity between technological and organisational activities. We assessed correlation between four of the innovations: two technological and two organisational. We found statistically significant positive Pearson correlation coefficients across all four innovations. While the strongest correlation is between the two technological (equipment and technology) and the two organisational innovations (work practices and staff training/skills development), there were also correlations between the technological and organisational types. The value of these ranged from 0.34 (between introducing new equipment and staff training/skills development) to 0.42 (between introducing new equipment and introducing new work practices). Introducing new technology was also correlated with introducing new work practices (0.39) and staff training/skills development (0.36). Table 9 also shows those industries that had generally high rates of introducing new work practices and equipment usually had proportionally similar rates of introducing new technology and equipment. The relationship is less apparent, however, with changes to supply chain and products, which had lower levels of engagement from businesses overall.

Table 5 provides further econometric analysis by high- and low-emission industries. Organisations in the agriculture, forestry and fishing; mining; manufacturing; and electricity, gas and water industries were deemed ‘high’ emitters; while all other industries were deemed ‘low’ emitters. The grouping of all other industries into a single category mirrors National Greenhouse Inventory reports. We also combined equipment and technology into one technological innovation category, and likewise for work practices, and training and skills development for organisational innovations.

Disaggregating industries into high and low emitters immediately improved the explanatory power of our predictive models, as measured by the percentage of correct predictions and the Nagelkerke R². A clear pattern emerges that the carbon price dominates the influence of cost reductions on the likelihood of high-emission industry organisations undertaking all emission reduction activities and innovations, in addition to the ‘anticipation’ effect of taking action since 2008. In most cases, the influence of the carbon price increases the probability of a high-emission industry organisation undertaking emission reduction activities or innovations by over 50 percentage points, likely linked to their being subject directly to the carbon price.

In contrast, we see that cost considerations typically dominate motivations in low-emission industries, particularly in the post-2008 model where we observe an influence of over 50 percentage points. This reinforces our argument for the indirect effect of the carbon price on firms not directly subject to the policy.

6. Discussion and conclusion

The progression of emissions in the lead up to and operation of the carbon price suggests that both the anticipated and implemented carbon price triggered a response from many businesses to reduce emissions. Conversely, periods of intense policy uncertainty, and the repeal of the carbon price, negatively affected the propensity to reduce emissions. The ‘signalling’ effect, therefore, appears not to have been restricted to a cost rise, but also operated in anticipation of rises.

The character, nature and extent of emissions reduction activities varied between industries. Direct emissions reductions came primarily from agriculture, forestry and fishing; electricity and utilities; and to some extent manufacturing. Other industries demonstrated little apparent direct effect from carbon pricing. However, in the case of the commercial services sector and manufacturing, there was a clear response in terms of Scope 2 emissions, indicating increased energy efficiency. With respect to the Porter hypothesis, this demonstrates that organisations were able to offset their regulatory burden with increased efficiency. While businesses did face costs as identified by Rametse (2015), his findings may understate the relevant savings.

Our findings also suggest, however, that key industries – that is, mining, transport and construction – may not have been sufficiently affected by carbon pricing to reduce net emissions. While our sample was too small to draw any definitive conclusions regarding mining, our survey data largely confirms the relatively low engagement of transport and construction overall. Further research is needed to identify why this occurred.

Our survey results largely confirm and add further nuance to aggregate data on carbon emissions. Most businesses in our survey reported acting consciously to reduce emissions, and overall the vast majority of firms took steps with the effect of reducing emissions, even where these behaviours were not linked explicitly to emissions. Importantly, our survey is not restricted to businesses directly subject to the carbon price and demonstrates that policy had an effect beyond the largest polluters. Moreover, the nature of the activities being undertaken involved increased efficiencies to offset part of the direct or indirect costs associated with carbon pricing. These included both investments in technological innovation and organisational innovation involving changes to staff and work practices. Overall, very few organisations resorted to purchasing carbon credits to reduce emissions. Critically, emissions activities also had a very small overall impact on employment among respondents.

We qualify our assessment of the extent to which carbon policy motivated this innovation. We find that only a limited number of organisations directly associated emissions reduction with the carbon price, though the rate was higher in certain carbon-intensive sectors. However, all of the other evidence – including the emphasis on cost and the timing of engagement with climate change – points to carbon policy as a key driver. The political milieu leading up to the implemented carbon price, and thus the anticipation of carbon pricing, therefore played as important a role in prompting action on climate change as the carbon price itself, which at the time of the survey seemed likely to be repealed. This may have been due not only to the anticipated effects of carbon pricing per se, but also to intensifying the attention paid by businesses to their carbon-polluting activities (Brannlund et al., 2014).

Two potential areas for future research might test these relationships further. One is the nature of business operations and markets in influencing the impact of regulation. It may be that global businesses would practice more sophisticated management of carbon emissions than smaller purely domestic businesses. Analysis of global business responses could also enable testing of the impact of regulations in one country compared with the impact in other countries with different regulatory frameworks. Second, differentiating between capital-intensive investment in larger organisations, such as new supply projects in electricity, and cost-reducing energy efficiencies introduced in smaller businesses would also yield a better understanding of regulatory impacts.

Overall, our findings broadly support the ‘Porter hypothesis’ that ‘properly designed environmental standards can trigger innovation that may partially or more than fully offset the costs of complying with them’ (Porter and van der Linde, 1995: 98). They do not support the ‘strong’ version – that is, we have no evidence that profitability was increased as a result of the carbon price. However, the findings go beyond the ‘weak’ version – we find evidence of innovation and of potentially cost-offsetting innovation and little reported negative effect on employment. Our findings also broadly support those of Borghesi et al. (2015) that organisational and technological innovations are important in tackling climate change and are complementary. Building upon these theories, our findings indicate that the anticipation of regulation arising from the political climate serves as an important trigger for firm-level technological and organisational innovations for reducing carbon emissions.

Footnotes

Final transcript accepted 1 November 2020 by Gautam Bose (AE Economics).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was part of a larger project supported by the then Australian Department of Industry, Innovation, Science, Research and Tertiary Education in 2013 (grant number DIISR13/00658).

ORCID iDs

Raymond Markey

Chris F Wright

Notes

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