Sustainable Well-Being and Sustainable Consumption and Production: An Efficiency Analysis of Sustainable Development Goal 12

Abstract

The core objective of Sustainable Development Goal (SDG) 12 is to do more and better with less, increasing net well-being gains from economic activities by reducing resource use, degradation, and pollution along the whole lifecycle while simultaneously improving quality of life. This paper quantifies the level of achievement of sustainable consumption and production in terms of efficiency scores. Due to data envelopment analysis, it is possible to monitor progress towards SDG 12, identifying the best performers to take as examples and the fields in which there is room for improvement. Although interesting differences emerge in countries’ efficiency, the results show that the best performers are OECD members, partners, and accession candidates. This underscores the crucial role of the OECD in advancing national sustainable development objectives.

1. Introduction

The unsustainable use of resources has triggered critical scarcities and caused climate change and widespread environmental degradation, all of which have negative impacts on the well-being of the planet and its inhabitants [1,2]. Building circularity and accelerating sustainable consumption and production patterns can create cost-effective solutions to advance the 2030 Agenda [3]. The latter explicitly highlights sustainable consumption and production (SCP) in SDG 12, which aims to promote resource and energy efficiency, sustainable infrastructure, and providing access to basic services, green and decent jobs, and a better quality of life for all [4,5].

Economic, trade, and fiscal policies are required to bolster these solutions, reinforcing nature-based cycles, growing natural wealth, and narrowing inequality gaps [6,7]. Consequently, the implementation of SCP as an integrated approach would help to achieve overall development plans and reduce potential economic, environmental, and social costs while strengthening economic competitiveness and reducing poverty [8,9]. Specifically, a primary goal of SCP is to decouple economic growth from environmental degradation by increasing the efficiency of resource utilisation in production, distribution, and product use [10,11]. This strategy aims to keep the energy, material, and pollution intensity of all production and consumption functions within the carrying capacities of natural ecosystems [12,13,14]. Hence, SCP requires a ‘lifecycle thinking’ approach to enhance the sustainable management of resources and achieve resource efficiency across both the production and consumption phases [15,16]. Only through this holistic view, SCP goals and actions can be powerful levers to accelerate the transition to an eco-efficient economy and turn environmental and social challenges into business and employment opportunities while disengaging economic growth from environmental degradation and preventing a rebound effect [17,18].

The core objective of SDG 12 is to do more and better with less, increasing net well-being gains from economic activities by reducing resource use, degradation, and pollution along the whole lifecycle while increasing the quality of life [19,20]. Such change requires collaboration among different stakeholders, including businesses, consumers, policymakers, researchers, scientists, retailers, media, and development cooperation agencies [21]. It necessitates a systemic approach and cooperation among actors operating in the supply chain, from producer to final consumer [22].

To summarise, SCP seeks to achieve a good life for everyone within the constraints of the Earth’s biophysical capacity. For this reason, it has been part of the international policy discourse for more than four decades [23], and the research shows that the efficiency approach contains essential elements for a transition to sustainability.

In other words, sustainable well-being integrates the pursuit of human happiness and quality of life with the need to preserve ecological and social systems. It recognises that individual and collective well-being cannot be sustained without a healthy environment and equitable social conditions [24]. SCP practices, as highlighted in SDG 12, are essential to achieve this balance by promoting the efficient use of resources, reducing environmental degradation, and fostering social equity [25]. Therefore, SCP is a necessary precondition for achieving long-term global well-being.

This intrinsic link between efficiency, sustainability, and well-being is the key-point of this work. A measurement of the percentage fulfilment of the SDG 12 is here proposed using the data envelopment analysis (DEA) approach, which might help in creating a more complex view on achieving the SDGs with respect to the resources used [26]. Moreover, DEA enables us to quantify not only the relative efficiency in reaching the goals, given the initial settings, but also the individual shortcomings of the countries in specific indicators with respect to the frontier [27,28]. More precisely, the aim is to assess how individual countries manage to meet measurable SDGs in terms of their capabilities. The novelty of this study with respect to previous studies lies in its methodological approach, which leverages DEA not just to measure the achievement of SDG 12 but to critically examine the efficiency with which countries utilise their distinct resources. Moreover, our research extends beyond the scope of the existing literature by incorporating a comparative analysis between OECD countries, which typically have more data and established policies, and non-OECD countries, which may face different challenges and opportunities in achieving SDG 12. This distinction provides insights into the varying efficiencies and policy impacts across different national contexts.

First, the distance between the current level of each country and the targets of SDG 12 is calculated using the distance function suggested by OECD [29]. Second, for each country, a score of the attainment level of the targets in the SDG 12 is proposed using DEA to assess the efficiency of each country in reaching this goal. Finally, an attempt is made to discern the impact of some fields of intervention on the achievement of the considered targets, providing insights into the areas in which policies can best drive the change.

2. Literature Review

In 2015, the United Nations adopted the 2030 Agenda for Sustainable Development, a framework designed to address global challenges, such as environmental degradation, social inequality, and economic instability. Central to this agenda is the focus on sustainable consumption and production (SCP) patterns, which is a key element of SDG 12. This goal emphasises the decoupling of economic growth from unsustainable resource use and emissions while improving the management of hazardous substances and waste [30]. The SDG 12 is structured into specific targets (i.e., sub-goals 12.1–12.8) and includes additional targets focused on the means of implementation (12.a, 12.b, 12.c), each with corresponding indicators.

The current literature predominantly emphasises production efficiency to improve resource use and mitigate environmental impact. For example, Gasper et al. (2019) [5] examined how SDG 12 frames SCP, highlighting the need for integrated approaches that consider both production efficiency and the socio-economic contexts of consumption. However, while this analysis provided a broad framework, it did not delve into the specifics of how different socio-economic contexts might affect SCP practices, revealing a gap in understanding the differential impacts of these practices across diverse settings.

Similarly, Velenturf and Purnell (2021) [31] advocated for principles of a sustainable circular economy, emphasising that sustainable development requires systemic changes throughout the resource lifecycle. However, the practical implementation of these principles across varying national contexts remains underexplored. Hickel (2020) [32] further introduced the Sustainable Development Index to measure ecological efficiency, yet there is a notable gap in the methodological consistency when comparing ecological efficiency metrics with traditional economic indicators, leading to potential discrepancies in evaluating countries’ performances.

Despite these contributions, there is still considerable uncertainty about how efficiently different countries use their resources to progress towards SCP. This uncertainty points to a significant gap in the literature, particularly in the comparative analysis of countries’ efficiencies in utilising their resources relative to their development capabilities [5,30]. Furthermore, as noted by Nikolova and Popova (2020) [33], the efficiency with which resources are used is not always aligned with improvements in subjective well-being, indicating a methodological limitation in linking objective measures of efficiency with subjective outcomes. Kozusznik et al. (2019) [34] also highlight this discrepancy in their study on office energy efficiency, which does not necessarily correlate with increased well-being or performance, thus revealing a gap in understanding the broader impacts of efficiency measures on quality of life.

Lafortune et al. (2018) [35] provided a foundational composite indicator for assessing progress towards the SDGs but did not address the input side—how efficiently countries utilise their available resources to achieve these outputs. This lack of focus on resource efficiency, particularly concerning SDG 12, highlights a gap in the literature that this study aims to address. The existing research, such as the work by Issever Grochová and Litzman (2021) [36], applied data envelopment analysis (DEA) to evaluate the efficiency of 172 countries concerning the SDGs but included only one indicator from SDG 12 (12.4.1). This limited scope suggests a gap in the methodological approach to assessing SDG 12 comprehensively, as it does not fully explore the efficiency of countries in achieving all targets of sustainable consumption and production.

Moreover, the use of composite indicators in the DEA literature, as discussed by Munda and Nardo (2009) [37], although valuable for ranking and comparison, often fails to capture the complex and multidimensional nature of sustainability in its entirety, especially when considering non-economic factors like social equity and ecological health. This points to a methodological gap in the current approaches that predominantly focus on linear and compensatory models.

This study addresses these gaps by focusing specifically on SDG 12 and employing a DEA approach to analyse data from 54 countries where sufficient information is available. By doing so, it aims to provide a more nuanced understanding of how countries can improve their efficiency in achieving sustainable consumption and production, thereby contributing a novel perspective to the existing body of research on sustainability.

The model development in this research is grounded in a thorough review of the existing literature on DEA applications to sustainable development goals, ensuring that our approach not only aligns with but also advances the current methodologies. By focusing on the specific indicators relevant to SDG 12, this study fills a critical gap in the literature by evaluating how countries can improve their efficiency in achieving sustainable consumption and production. Considering the unique social, economic, and environmental contexts of each country, this approach offers new insights into the varying efficiencies and policy impacts across different national contexts, providing guidance for potential revisions to strategies aimed at achieving SDG 12 by 2030.

Through this review and methodological development, the study enhances our understanding of sustainability practices globally and presents a refined model for assessing progress towards SCP, specifically tailored to the capacities and contexts of each country included in the analysis.

3. Methodology

To assess the current level of SDG achievement and the progress made by each country since their adoption, it is crucial to compare the progress with the sustained resource efforts. The adoption of these goals by 193 countries introduces considerable heterogeneity among observations. Some countries may have a more favourable initial point, with significantly better indicators compared to other countries, making goal attainment relatively easier for them. Conversely, countries starting with a much worse situation in 2015 may encounter difficulties in achieving the goals despite their efforts.

Using DEA, it is possible to compute the relative efficiency in achieving the SDGs with respect to the countries’ background in sustainable development. More precisely, a measure of the distance between one country’s situation and the optimum is calculated, with the optimum level determined by the set of countries and indicators. DEA is a linear programming technique for assessing production performance, providing a deterministic relationship between resource inputs and outcomes [38,39]. It incorporates multiple inputs and outputs simultaneously [40]. The method calculates a relative efficiency score for decision-making units based on an optimally weighted allocation for a set of inputs and outputs. The countries considered in this analysis receive scores ranging from 0 (not efficient at all) to 1 (efficient units), with the efficient units forming a production frontier that envelopes others and to which all inefficient units are compared. DEA models quantify efficiency as the weighted sum of outputs to the weighted sum of inputs, assessing how effectively countries convert inputs into outputs. Inefficiency is then represented by the ratio of actual to ‘optimal’ performance.

DEA models can be input-oriented or output-oriented. The input-oriented approach minimises inputs for a constant set of outputs, while the output-oriented approach maximises outputs while holding inputs constant. This paper adopts an output-oriented DEA because changes to inputs are unlikely to take place in the short term within complex national production systems. This choice aligns with the existing literature, which predominantly favoured an output-orientation approach when assessing efficiency in the pursuit of sustainable development. Additionally, the output orientation, combined with variable returns to scale (VRS-DEA), seems to be the most appropriate as it allows for accurate processing in the presence of units of different sizes.

The DEA approach addresses two key issues [41]:

It provides information on the current state, i.e., how efficient each country with its capabilities is relative to the technical optimum;

It provides guidance for potential revising strategies for achieving the SDGs by 2030. The methodology of efficiency analysis introduced here is novel compared to the approach employed by OECD in measuring the distance from SDG targets, which is the first step in this work. OECD calculates a standardised distance from a country’s current situation to the end-value (target level). In particular, for measuring the distance from achieving the target, OECD uses a specific process to set end-values. Wherever possible, target levels specified in the 2030 Agenda are used (called type-A targets). When no target value is defined in the text of the 2030 Agenda, target levels were drawn from other international agreements or based on OECD expert judgment (type-B targets). If neither the 2030 Agenda nor expert sources provide a target value, the target level is based on the current “best performance” among OECD countries (type-C targets). The best performance is calculated as the level attained by the top 10% of OECD countries. In the case of Goal 12, there are no ending values set by the text of the 2030 Agenda, nor values univocally identified from international agreements. For consistency with the DEA methodology used in the second step, the target values are detected by considering the current best performance, i.e., the value of the top 10% of performers among the 54 countries considered.

After defining a target value, the performance across different targets is compared by normalising indicator values using a modified z-score formula:

$$z=\mathrm{m}\mathrm{a}\mathrm{x}\left({\displaystyle \frac{(T-x_i)}{\sigma }},0\right)$$

where T is the target level, $x_i$ is the current situation of country i, and σ refers to the standard deviation among countries in the most recent year. Using this formula, the higher the distance, the further the country needs to travel to achieve its target. A distance of zero indicates the country has already achieved the 2030 target. When the country has already exceeded the target, the value obtained through this formula will be 0, so there is no premium for going beyond the target. Once the standardised distance is calculated using the OECD methodology, DEA is used to assess the countries’ efficiency in achieving their current situation in SDG indicators. A comparison between the two methods is made: the former considers the distance from the target, even if it tries to take into account the context (since the standard deviation of the groups is included in the formula); the latter measures the distance considering both the referring group and the resources used by each single country in the group.

In classical radial DEA models, slacks are measures that relate to further increases in output or reduction in input that could be gained beyond the ones implied by the radial projection. In other words, radial DEA scores suggest proportional changes in inputs and outputs without taking into account the input excesses and output shortfalls [42]. Sometimes, units that result as efficient can still improve their performance by taking action on some particular inputs and/or outputs instead of considering them as a whole. Neglecting the role of slacks could lead to ‘weak efficiency’, while ‘strong efficiency’ is achieved with slack-based models [43]. Additionally, examining slacks can suggest specific policy interventions in fields where even generally efficient countries have room for improvement. For technical details of slacks in DEA, see [44].

Following [40], to avoid the ‘curse of dimensionality’ [45,46], the original dataset is reduced with the least possible loss of information by aggregating the three output variables referring to SDG 12.8.1, which are just three different aspects of the extent to which global citizenship education and education for sustainable development are mainstreamed in tools for everyday life in the education system. Hence, DEA is implemented using two inputs and nine outputs. The countries’ efficiency score heavily relies on the sample size and the number of variables involved. The greater the number of variables, the less discerning the DEA is [47,48]. The literature indicates empirical rules regarding the number of units versus the number of inputs and outputs: sometimes the number of units is required to be at least twice the number of inputs and outputs [49]; sometimes, it is required to be at least three times the number of inputs and outputs [50]; finally, the number of units is required to be at least twice the product of the number of inputs and the number of outputs [51]. The sample of 54 countries satisfies all three proposed rules.

4. Data

The pursuit of the 2030 Agenda envisions a world where sustained, inclusive, and sustainable economic growth is enjoyed by every country [52]. However, the lack of specificity in some targets and the absence of consistent indicators measured annually for every country pose challenges in gauging inclusive and sustainable growth.

The SDG indicators are classified into three tiers, where Tier 1 indicators have established methodology and good data coverage, Tier 2 indicators have methodologies but lack adequate data coverage, and Tier 3 indicators lack both sound method and adequate data.

Among the SDG 12 indicators, 8 are classified as Tier 2 and 5 as Tier 1, with none falling under Tier 3, highlighting the limited data coverage to measure countries’ progress towards SCP. This research is based on official data from the World Bank and the United Nations for the last available year (2021), acknowledging potential data incompleteness.

The selection of countries depends on the need to include those with the maximum number of indicators. The dataset includes all OECD countries plus other countries worldwide. The methodological challenges are recognised, including potential changes in the number of indicators and methodologies over time, varying data quality across countries. Despite these challenges, the study adheres to the United Nations goals without questioning their content, refraining from including proxy indicators to avoid misleading interpretations [53].

The final dataset includes 11 indicators among the 13 targets.

Table 1. SDG 12 indicators.

Target	Indicators
12.2.1	Material footprint
12.2.2	Domestic material consumption
12.4.2	Hazardous waste generated
12.5.1	Municipal waste recovered (composting and recycling)
12.6.1	Number of companies publishing sustainability reports
12.7.1	Sustainable public procurement policies and action plans
12.8.1 (1)	Extent to which global citizenship education and education for sustainable development are mainstreamed in curricula
12.8.1 (2)	Extent to which global citizenship education and education for sustainable development are mainstreamed in teacher education
12.8.1 (3)	Extent to which global citizenship education and education for sustainable development are mainstreamed in national education policies
12.b	Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism sustainability
12.c	Amount of fossil-fuel subsidies (production and consumption) per unit of GDP

reports the SDG 12 indicators available for the whole group of countries that have been used for the analysis.

The missing targets are 12.1, 12.3, and 12.a. The latter is measured by the indicator ’Installed renewable energy-generating capacity in developing countries’. This means that data incompleteness arises from the availability of values exclusively for developing countries. Since DEA assesses the efficiency of decision-making units in using resources to produce outputs, the analysis requires a careful selection of indicators to serve as inputs and outputs. This selection process follows the guidance of [36], as well as the DEA literature strand that treats variables to be minimised as inputs and all others as outputs [51]. The final list of inputs and outputs, along with their summary statistics, is presented in

Table 2. Descriptive statistics of SDG 12′s targets.

		Mean	St. Dev.	Skewness	Kurtosis	Min	Max	Target
Input	12.2.1 Material footprint	0.07	0.20	3.22	10.41	0	1	0
	12.4.2 Hazardous waste per capita	2132.35	10,580.43	7.17	52.11	2.78	78,514.69	32.05
Output	12.2.2 Domestic material consumption	19.59	10.80	1.88	3.78	7.95	56.69	32.31
	12.5.1 Municipal waste recovered	5594.09	13,168.89	4.65	25.21	0.10	85257	14173
	12.6.1 Number of companies publishing sustainability reports	105.07	207.28	3.75	15.50	1	1169	252
	12.7.1 Sustainable public procurement policies and action plans	2.30	0.71	−0.19	−0.50	1	4	3
	12.8.1 (1) Global citizenship education and education for sustainable development in curricula	0.75	0.18	−1.33	1.32	0.24	0.99	0.92
	12.8.1 (2) Global citizenship education and education for sustainable development in teacher education	0.75	0.19	−0.89	0.04	0.20	1	0.95
	12.8.1 (3) Global citizenship education and education for sustainable development in national education policies	0.80	0.20	−0.85	−0.48	0.35	1	1

.

5. Results

Table 3. Standardised distances (Part I).

Country	12.2.1 Material Footprint	12.2.2 Domestic Material Consumption	12.4.2 Hazardous Waste per Capita	12.5.1 Municipal Waste Recovered	12.6.1 Number of Companies Publishing Sustainability Reports	12.7.1 Sustainable Public Procurement Policies and Action Plans
Argentina	0.000	1.416	0.001	0.995	1.095	1.407
Australia	0.0001	0.000	0.066	0.689	0.048	0.000
Austria	0.000	1.488	0.011	0.727	1.052	0.000
Belgium	1.9975	1.368	0.029	0.750	1.018	0.000
Bulgaria	0.000	1.187	0.186	1.001	1.139	0.000
Canada	0.0068	0.000	0.032	0.324	0.072	1.407
Chile	0.000	0.000	0.056	1.073	1.061	1.407
China	0.000	0.882	0.002	1.001	0.000	0.000
Colombia	2.3690	2.254	0.332	1.065	1.148	1.407
Costa Rica	0.000	1.969	0.086	1.069	1.139	0.000
Croatia	0.000	2.082	0.001	1.038	1.090	0.000
Cyprus	0.000	0.329	0.001	1.037	1.158	1.407
Denmark	0.000	1.135	0.019	0.914	0.936	1.407
Estonia	0.000	0.382	0.110	1.065	1.114	1.407
Finland	0.000	0.067	0.053	0.968	0.902	0.000
France	0.000	1.879	0.013	0.000	0.492	0.000
Germany	0.000	1.611	0.024	0.000	0.275	1.407
Greece	0.0001	1.902	0.002	0.987	1.153	1.407
Hungary	0.0077	1.483	0.002	0.981	1.187	1.407
Iceland	0.000	0.566	0.008	1.072	1.177	0.000
Ireland	0.000	0.692	0.014	0.978	1.037	0.000
Israel	0.0136	0.867	0.143	0.981	1.177	1.407
Italy	0.000	2.220	0.013	0.000	0.762	0.000
Japan	0.000	2.150	0.216	0.444	0.000	1.407
Latvia	0.000	1.684	0.004	1.049	1.129	0.000
Lithuania	0.000	1.470	0.006	1.030	1.110	0.000
Luxembourg	0.0145	0.987	0.034	1.056	1.100	0.000
Malaysia	0.000	1.034	0.009	1.062	0.000	1.407
Malta	0.0093	1.887	0.005	1.052	1.192	0.000
Mexico	0.000	1.922	0.657	0.895	0.950	1.407
Moldova	0.000	2.106	0.003	1.074	1.153	2.815
Mongolia	0.000	0.000	0.006	1.075	1.211	2.815
Netherlands	0.000	1.652	0.024	0.674	0.921	0.000
New Zealand	0.000	0.000	0.620	0.053	0.999	1.407
Norway	0.000	0.593	0.030	0.976	0.888	1.407
Peru	0.000	1.119	0.003	1.072	1.134	1.407
Philippines	0.000	2.230	0.001	1.073	1.071	1.407
Poland	3.4990	1.375	0.003	0.691	1.071	0.000
Portugal	0.000	1.455	0.006	0.969	1.168	0.000
Romania	0.0161	1.663	0.001	1.026	1.182	2.815
Russia	0.000	1.583	0.074	1.065	1.134	1.407
Serbia	0.000	1.417	0.143	1.057	1.163	1.407
Singapore	0.0002	1.080	0.003	1.064	0.811	2.815
Slovak Republic	0.0001	1.853	0.005	0.988	1.100	1.407
Slovenia	2.7297	1.752	0.003	1.030	1.201	0.000
South Africa	2.2888	1.835	0.084	0.000	0.700	1.407
Spain	0.0326	2.134	0.004	0.399	0.912	2.815
Sweden	0.0139	0.933	0.069	0.947	0.169	1.407
Switzerland	0.000	1.971	0.028	0.832	0.589	0.000
Turkey	0.000	0.945	0.031	0.753	0.844	2.815
Ukraine	0.000	1.792	0.002	1.076	1.206	2.815
The United Kingdom	0.000	1.940	0.006	0.063	0.000	1.407
The United States	5.0492	0.834	7.418	0.000	0.000	0.000
Uruguay	0.000	1.293	0.037	0.675	1.206	1.407

and

Table 4. Standardised distances (Part II).

Country	12.8.1 Curricula	12.8.1 2 Teacher Education	12.8.1 3 National Education Policies	12.b Tools to Monitor Tourism Sustainability	12.c Fossil-Fuel Subsidies
Argentina	1.574	1.861	1.910	0.845	0.000
Australia	0.562	0.362	0.539	0.000	1.040
Austria	0.787	1.292	0.833	0.282	1.612
Belgium	0.225	0.775	0.245	0.282	0.918
Bulgaria	1.518	1.137	2.155	1.971	0.000
Canada	0.787	1.292	0.588	0.845	1.680
Chile	0.225	0.444	0.686	1.126	0.986
China	1.630	1.447	1.763	2.534	1.720
Colombia	0.225	0.517	0.000	0.000	0.252
Costa Rica	0.956	1.085	1.176	2.534	1.761
Croatia	3.823	3.049	2.890	1.408	1.040
Cyprus	0.000	0.000	0.000	1.971	1.571
Denmark	1.349	0.930	1.225	0.000	1.340
Estonia	0.506	0.000	0.588	1.971	1.734
Finland	0.618	0.517	0.588	0.563	1.136
France	0.000	0.000	0.000	2.534	1.408
Germany	0.112	0.000	0.000	1.971	1.639
Greece	3.429	2.222	2.155	1.971	0.496
Hungary	0.337	0.103	0.000	0.000	1.720
Iceland	0.169	0.207	0.245	0.282	1.938
Ireland	0.618	0.517	0.588	1.408	1.924
Israel	0.899	1.292	1.225	2.252	1.503
Italy	0.225	0.775	0.588	1.971	1.027
Japan	0.506	1.757	1.078	2.534	1.843
Latvia	0.337	0.000	0.000	1.971	1.122
Lithuania	0.394	0.258	0.000	0.000	0.496
Luxembourg	1.012	1.189	1.029	0.000	1.938
Malaysia	0.225	0.258	0.588	0.563	0.782
Malta	1.124	0.258	0.784	1.971	1.938
Mexico	0.843	0.775	1.225	0.000	0.000
Moldova	0.899	1.034	0.686	2.534	0.891
Mongolia	0.956	0.517	0.588	2.534	1.748
Netherlands	0.112	0.724	0.882	0.845	1.707
New Zealand	1.911	1.809	3.184	1.126	1.924
Norway	0.000	0.000	0.049	1.971	1.816
Peru	0.618	3.876	0.000	2.534	1.829
Philippines	2.867	2.584	2.939	0.000	1.924
Poland	0.675	0.258	0.000	2.534	1.938
Portugal	2.642	2.016	1.910	1.408	1.530
Romania	0.000	0.000	0.000	0.282	1.448
Russia	0.675	0.258	0.000	2.252	0.000
Serbia	3.766	3.153	2.988	1.971	1.000
Singapore	0.787	1.602	1.420	2.534	1.938
Slovak Republic	1.574	1.861	2.400	0.000	1.924
Slovenia	0.000	0.517	0.000	0.000	1.598
South Africa	1.518	2.222	2.498	0.563	0.551
Spain	0.056	0.000	0.000	1.126	1.748
Sweden	0.675	0.000	0.000	0.282	1.299
Switzerland	1.124	1.344	0.980	1.971	1.639
Turkey	0.225	0.258	0.000	2.252	1.448
Ukraine	0.000	0.000	0.000	2.534	0.000
United Kingdom	1.855	2.429	2.890	0.563	1.938
United States	2.249	2.636	1.910	2.252	1.870
Uruguay	2.979	2.998	3.086	1.126	1.938

report the distances of countries’ current situations from the optimal value for the indicators measuring SDG 12. It is important to note that these distances are calculated as standardised distances from the target level, following the OECD methodology [54].

First, it is essential to define the target level according to the scope and objective of the analysis. In this study, focusing on identifying the best performers in achieving SDG 12 efficiency, the target level was set to reflect the top 10% of performers. This benchmark provides a clear standard against which countries’ performances are measured. Once this benchmarking is established, the indicators are normalised to facilitate comparison across countries.

As detailed in Section 3, countries that exceed the target value receive no additional benefit; therefore, a distance value of 0 indicated that the country has already achieved the 2030 target. Conversely, the distance to the target level increases as the value moves away from 0, indicating greater room for improvement.

The analysis reveals that countries like France, Germany, Latvia, Lithuania, Slovenia, and Ukraine are closest to the 2030 targets, having achieved the target value for at least 4 out of the 11 indicators. Meanwhile, Australia, China, Italy, Norway, Romania, and the United States have met the end value for three indicators. Interestingly, apart from Ukraine, China, and Romania, OECD countries generally outperform the rest of the world. This trend suggests that OECD countries may have more robust policies and practices promoting sustainable consumption and production, as evidenced by the higher number of countries with a 0-distance value in the material footprint indicator. This success may be attributed to the OECD’s Green Growth Strategy [55], which supports countries in promoting economic growth while reducing greenhouse gas emissions, pollution, and inefficient resource use, all while maintaining biodiversity. This strategy aims to decouple economic growth from environmental impacts and enhance overall well-being.

For non-OECD countries, the performance appears more mixed. While some non-OECD countries like Ukraine show strong performance, this is often due to close collaboration with OECD initiatives, such as Ukraine’s involvement in the OECD-Ukraine Country Programme launched in June 2023. This program aims to support Ukraine’s reforms and guide its recovery, reconstruction, and efforts towards EU and OECD accession, indicating that strong external support and targeted policies can significantly enhance sustainability outcomes.

When considering the resources used in each country’s process, a more nuanced picture emerges, as shown in

Table 5. DEA results (Part I).

Country	Efficiency Score	Presence of Slacks	12.2.1 Material Footprint	12.4.2 Hazardous Waste per Capita	12.2.2 Domestic Material Consumption
Argentina	0.749	TRUE	0.005	0.057	0.000
Australia	1.000	FALSE	0.000	0.000	0.000
Austria	0.931	TRUE	0.565	0.147	0.000
Belgium	0.992	TRUE	0.000	0.026	0.000
Bulgaria	1.000	TRUE	0.160	0.022	0.480
Canada	1.000	FALSE	0.000	0.000	0.000
Chile	1.000	TRUE	0.000	0.480	0.360
China	0.996	TRUE	0.723	0.723	0.000
Colombia	1.000	TRUE	0.824	0.614	0.000
Costa Rica	0.852	TRUE	0.347	0.337	0.089
Croatia	0.814	TRUE	0.457	0.458	0.195
Cyprus	1.000	TRUE	0.000	0.000	0.000
Denmark	1.000	FALSE	0.000	0.000	0.000
Estonia	0.910	TRUE	0.014	0.000	0.000
Finland	0.966	TRUE	0.070	0.069	0.000
France	1.000	FALSE	0.000	0.000	0.000
Germany	1.000	FALSE	0.000	0.000	0.000
Greece	0.455	TRUE	0.198	0.209	0.057
Hungary	0.982	TRUE	0.300	0.001	0.000
Iceland	1.000	FALSE	0.000	0.000	0.000
Ireland	0.943	TRUE	0.122	0.124	0.000
Israel	0.699	TRUE	0.014	0.000	0.000
Italy	0.936	TRUE	0.138	0.139	0.135
Japan	0.808	TRUE	0.298	0.270	0.123
Latvia	0.981	TRUE	0.038	0.040	0.000
Lithuania	1.000	TRUE	0.010	0.001	0.000
Luxembourg	0.974	TRUE	0.411	0.051	0.000
Malaysia	1.000	FALSE	0.000	0.000	0.000
Malta	0.900	TRUE	0.328	0.222	0.050
Mexico	0.840	TRUE	0.270	0.601	0.523
Moldova	0.742	TRUE	0.000	0.001	0.049
Mongolia	0.930	TRUE	0.000	0.006	0.000
Netherlands	0.931	TRUE	0.476	0.146	0.000
New Zealand	0.716	TRUE	0.156	0.079	0.000
Norway	1.000	FALSE	0.000	0.000	0.000
Peru	0.627	TRUE	0.000	0.003	0.000
Philippines	0.833	TRUE	0.228	0.013	0.151
Poland	1.000	TRUE	0.125	0.007	0.000
Portugal	0.794	TRUE	0.625	0.519	0.012
Romania	1.000	FALSE	0.000	0.000	0.000
Russia	1.000	FALSE	0.000	0.000	0.000
Serbia	0.435	TRUE	0.300	0.291	0.000
Singapore	0.654	TRUE	0.000	0.004	0.000
Slovak Republic	0.833	TRUE	0.210	0.011	0.060
Slovenia	1.000	TRUE	0.104	0.003	0.135
South Africa	1.000	FALSE	0.000	0.000	0.000
Spain	0.958	TRUE	0.000	0.000	0.093
Sweden	1.000	FALSE	0.000	0.000	0.000
Switzerland	0.853	TRUE	0.445	0.342	0.096
Turkey	0.947	TRUE	0.003	0.000	0.000
Ukraine	1.000	TRUE	0.104	0.000	0.000
The United Kingdom	0.992	TRUE	0.394	0.395	0.204
The United States	1.000	FALSE	0.000	0.000	0.000
Uruguay	0.547	TRUE	0.234	0.081	0.000

,

Table 6. DEA results (Part II).

Country	Efficiency Score	Presence of Slacks	12.5.1 Municipal Waste Recovered	12.6.1 Number of Companies Publishing Sustainability Reports	12.7.1 Sustainable Public Procurement Policies and Action Plans
Argentina	0.749	TRUE	0.015	0.047	0.035
Australia	1.000	FALSE	0.000	0.000	0.000
Austria	0.931	TRUE	0.099	0.133	0.000
Belgium	0.992	TRUE	0.000	0.025	0.000
Bulgaria	1.000	TRUE	0.210	0.380	0.054
Canada	1.000	FALSE	0.000	0.000	0.000
Chile	1.000	TRUE	0.190	0.009	0.000
China	0.996	TRUE	0.749	0.000	0.238
Colombia	1.000	TRUE	0.235	0.000	0.000
Costa Rica	0.852	TRUE	0.461	0.416	0.000
Croatia	0.814	TRUE	0.454	0.440	0.000
Cyprus	1.000	TRUE	0.000	0.000	0.000
Denmark	1.000	FALSE	0.000	0.000	0.000
Estonia	0.910	TRUE	0.029	0.015	0.000
Finland	0.966	TRUE	0.113	0.158	0.000
France	1.000	FALSE	0.000	0.000	0.000
Germany	1.000	FALSE	0.000	0.000	0.000
Greece	0.455	TRUE	0.175	0.188	0.000
Hungary	0.982	TRUE	0.000	0.019	0.096
Iceland	1.000	FALSE	0.000	0.000	0.000
Ireland	0.943	TRUE	0.199	0.216	0.000
Israel	0.699	TRUE	0.069	0.070	0.000
Italy	0.936	TRUE	0.059	0.133	0.000
Japan	0.808	TRUE	0.301	0.000	0.354
Latvia	0.981	TRUE	0.164	0.124	0.000
Lithuania	1.000	TRUE	0.089	0.059	0.000
Luxembourg	0.974	TRUE	0.072	0.092	0.000
Malaysia	1.000	FALSE	0.000	0.000	0.000
Malta	0.900	TRUE	0.321	0.292	0.000
Mexico	0.840	TRUE	0.891	0.400	0.200
Moldova	0.742	TRUE	0.082	0.046	0.313
Mongolia	0.930	TRUE	0.001	0.026	0.333
Netherlands	0.931	TRUE	0.124	0.122	0.000
New Zealand	0.716	TRUE	0.000	0.276	0.168
Norway	1.000	FALSE	0.000	0.000	0.000
Peru	0.627	TRUE	0.104	0.064	0.000
Philippines	0.833	TRUE	0.015	0.000	0.000
Poland	1.000	TRUE	0.016	0.130	0.076
Portugal	0.794	TRUE	0.502	0.515	0.000
Romania	1.000	FALSE	0.000	0.000	0.000
Russia	1.000	FALSE	0.000	0.000	0.000
Serbia	0.435	TRUE	0.319	0.360	0.000
Singapore	0.654	TRUE	0.111	0.000	0.574
Slovak Republic	0.833	TRUE	0.000	0.008	0.000
Slovenia	1.000	TRUE	0.000	0.066	0.514
South Africa	1.000	FALSE	0.000	0.000	0.000
Spain	0.958	TRUE	0.000	0.002	0.215
Sweden	1.000	FALSE	0.000	0.000	0.000
Switzerland	0.853	TRUE	0.382	0.274	0.000
Turkey	0.947	TRUE	0.035	0.000	0.486
Ukraine	1.000	TRUE	0.000	0.013	0.397
The United Kingdom	0.992	TRUE	0.252	0.000	0.260
The United States	1.000	FALSE	0.000	0.000	0.000
Uruguay	0.547	TRUE	0.000	0.172	0.000

and

Table 7. DEA results (Part III).

Country	Efficiency Score	12.8.1 Global Citizen Education and Education for Sustainable Development Mainstreamed in Educational System	12.b Tools to Monitor Tourism Sustainability	12.c Fossil-Fuel Subsidies
Argentina	0.749	0.046	0.000	0.000
Australia	1.000	0.000	0.000	0.000
Austria	0.931	0.077	0.000	0.000
Belgium	0.992	0.000	0.000	0.000
Bulgaria	1.000	0.470	0.880	0.260
Canada	1.000	0.000	0.000	0.000
Chile	1.000	0.320	0.060	0.008
China	0.996	0.000	0.138	0.002
Colombia	1.000	0.000	0.719	0.000
Costa Rica	0.852	0.000	0.032	0.027
Croatia	0.814	0.507	0.000	0.000
Cyprus	1.000	0.003	0.001	0.000
Denmark	1.000	0.000	0.000	0.000
Estonia	0.910	0.000	0.000	0.035
Finland	0.966	0.000	0.000	0.000
France	1.000	0.000	0.000	0.000
Germany	1.000	0.000	0.000	0.000
Greece	0.455	0.025	0.000	0.000
Hungary	0.982	0.000	0.000	0.037
Iceland	1.000	0.000	0.000	0.000
Ireland	0.943	0.000	0.000	0.088
Israel	0.699	0.000	0.057	0.000
Italy	0.936	0.000	0.000	0.000
Japan	0.808	0.000	0.027	0.045
Latvia	0.981	0.000	0.000	0.000
Lithuania	1.000	0.084	0.221	0.023
Luxembourg	0.974	0.173	0.000	0.079
Malaysia	1.000	0.000	0.000	0.000
Malta	0.900	0.000	0.000	0.064
Mexico	0.84	0.030	0.049	0.62
Moldova	0.742	0.000	0.000	0.000
Mongolia	0.930	0.000	0.444	0.119
Netherlands	0.931	0.000	0.000	0.013
New Zealand	0.716	0.277	0.000	0.096
Norway	1.000	0.000	0.000	0.000
Peru	0.627	0.000	0.167	0.081
Philippines	0.833	0.510	0.000	0.133
Poland	1.000	0.909	0.769	0.076
Portugal	0.794	0.145	0.000	0.000
Romania	1.000	0.000	0.000	0.000
Russia	1.000	0.000	0.000	0.000
Serbia	0.435	0.405	0.000	0.000
Singapore	0.654	0.000	0.223	0.114
Slovak Republic	0.833	0.240	0.000	0.126
Slovenia	1.000	0.000	0.000	0.052
South Africa	1.000	0.000	0.000	0.000
Spain	0.958	0.000	0.000	0.045
Sweden	1.000	0.000	0.000	0.000
Switzerland	0.853	0.000	0.000	0.000
Turkey	0.947	0.000	0.022	0.000
Ukraine	1.000	0.037	0.417	0.000
The United Kingdom	0.992	0.222	0.000	0.064
The United States	1.000	0.000	0.000	0.000
Uruguay	0.547	0.491	0.000	0.098

.

Here, the focus is on developments in decoupling environmental pressures from economic growth, advancing the green economy and managing waste generation and disposal. Out of the 54 countries analysed, 21 are efficient in achieving SDG 12 targets: Australia, Bulgaria, Canada, Chile, Colombia, Cyprus, Denmark, France, Germany, Iceland, Lithuania, Malaysia, Norway, Poland, Romania, Russia, Slovenia, South Africa, Sweden, Ukraine, and the United States. However, not all these countries are fully efficient; some, like Bulgaria, Chile, Colombia, Cyprus, Lithuania, Poland, Slovenia, and Ukraine, have slack values indicating potential for further improvement in specific areas.

For example, Colombia’s slack value of 0.824 for material footprint suggests it could reduce its material footprint by 82.4% without altering its production technology. Additionally, Colombia could enhance its SDG 12 efficiency by targeting reductions in hazardous waste generation (61.4%), improving municipal waste recovery (23.5%), and promoting more sustainable tourism (71.9%). Similarly, Poland has opportunities for improvement across various areas except for domestic material consumption. This finding points to a broader issue, as the relationship between material footprint and consumption remains complex; despite efforts, many regions have successfully reduced domestic material consumption while their overall material footprint continues to rise [20]. This suggests that political efforts should move beyond simply increasing efficiency and focus more on reducing overall consumption.

There is a crucial need to identify specific problems and develop dedicated solutions for sustainable practices. Even countries with relatively minor slack values, like Cyprus, can still make significant strides in areas such as tourism sustainability and educational systems, demonstrating that there are always opportunities for improvement regardless of initial performance.

Malaysia’s fully efficient status aligns with the World Bank’s projection of its potential transition to a high-income economy between 2024 and 2028, reflecting its significant economic transformation over recent decades [56]. However, the World Bank also emphasises the need for Malaysia to continue enhancing the sustainability of its economic growth to maintain this trajectory.

Among both fully efficient and not fully efficient countries, it is notable that there are no non-OECD countries, highlighting the substantial influence of OECD membership or partnership on sustainability outcomes. OECD’s comprehensive support mechanisms, including data collection and monitoring tools, create a robust framework for member countries to track and improve their sustainability practices [57]. Moreover, OECD partnerships foster synergies between private and public sectors and across domestic and international boundaries, providing strong support systems for countries as they work towards a more sustainable future [58].

Successful progress towards the SDGs requires balancing social, environmental, and economic objectives, promoting socio-economic development while preserving the planet’s resources and ecosystems, combating climate change, and encouraging the use of renewable energy sources [59,60]. The analysis of standardised distances highlights the importance of targeted policies and international cooperation in achieving these goals, particularly in the context of SDG 12.

The differences in outcomes achieved by countries in progressing towards SDG 12 goals can largely be attributed to the distinct policies and approaches adopted by OECD member countries compared to non-OECD countries.

OECD countries typically have more established and rigorous policies regarding sustainable consumption and production, supported by common guidelines and a strong commitment to international standards. These policies often include the promotion of clean technologies, the implementation of stricter regulations for waste management and resource efficiency, and economic incentives to encourage sustainable behaviours among both businesses and consumers.

Furthermore, OECD member countries benefit from a network of cooperation and knowledge-sharing that facilitates the adoption of innovative practices and the dissemination of effective solutions. Through initiatives such as the Green Growth Strategy and other sustainability programs, the OECD provides its members with analytical tools, technical support, and platforms for policy dialogue, contributing to more rapid and targeted progress towards sustainable development goals. This support is often less accessible to non-OECD countries, which may not have the same resources or access to knowledge and support networks.

In contrast, non-OECD countries may face unique challenges due to different economic, political, and social contexts. For instance, many non-OECD countries must balance economic development objectives with the need to protect the environment, often with limited resources and reduced access to advanced technology. These conditions can slow progress towards SDG 12, especially in the absence of well-defined national policies or targeted international support. However, some non-OECD countries are making significant progress through strategic partnerships and cooperation with international organizations, as evidenced by the OECD-Ukraine program.

It is important to recognise that while differences in outcomes can be attributed to national policies and the level of international support, they also reflect the importance of tailoring strategies to specific country contexts. This approach ensures that efforts towards achieving SDG 12 are both effective and sustainable, taking into account the diverse economic, environmental, and social realities faced by different countries.

The differences in DEA efficiency scores observed in Table 5, Table 6 and Table 7 can be attributed to several key factors that influence each country’s performance in achieving SDG 12 targets. These variations in scores highlight the diverse economic structures, resource endowments, policy frameworks, and stages of development among the countries analysed. The DEA model measures relative efficiency, meaning that countries are evaluated against each other based on their ability to convert inputs (such as natural resources, economic capital, and technological capabilities) into desired outputs (such as reduced waste, lower emissions, and sustainable consumption patterns). Therefore, countries with different input–output relationships will naturally exhibit varying efficiency scores.

One primary reason for these differences is the variation in resource allocation and utilisation strategies among countries. For instance, some countries may have more advanced technologies and infrastructures that enable them to use resources more efficiently, resulting in higher DEA scores. Conversely, countries with less access to technology or lower levels of investment in sustainable infrastructure may appear less efficient in the DEA analysis. Additionally, the regulatory environment and policy priorities in each country play a significant role. Countries with stringent environmental regulations and strong government incentives for sustainable practices are likely to perform better in the DEA analysis, as these policies drive more efficient use of resources and greater progress towards SDG 12.

Another factor contributing to the differences in DEA efficiency scores is the economic context within which each country operates. For example, countries with economies heavily reliant on resource-intensive industries may face greater challenges in improving their efficiency scores compared to countries with more diversified or service-oriented economies. The former may require more substantial changes in production processes and consumption patterns to achieve similar improvements in efficiency. Additionally, differences in economic development levels can affect a country’s capacity to invest in and implement sustainability measures, influencing their DEA results.

Moreover, the DEA methodology itself is sensitive to the selection of inputs and outputs used in the analysis. The choice of indicators reflecting resource use, waste generation, and consumption patterns can significantly impact the efficiency scores. Some countries might excel in specific areas (e.g., waste management) but lag in others (e.g., reducing material footprint), resulting in varied overall efficiency scores. This variation underscores the importance of selecting appropriate and comprehensive indicators that accurately capture the multi-dimensional nature of sustainable development.

Lastly, cultural and societal factors may also influence the DEA results. Societies with a higher awareness of and commitment to sustainability may adopt behaviours and practices that contribute to higher efficiency scores. On the other hand, in countries where sustainability is less prioritised by the public or where cultural practices inherently involve higher resource consumption, the DEA scores may be lower.

In conclusion, the observed differences in DEA efficiency scores among countries are a reflection of the complex interplay between technological, economic, policy, and cultural factors. These findings emphasise the need for tailored policy interventions that consider each country’s unique context and challenges in achieving SDG 12. By understanding the underlying reasons for these differences, policymakers can design more effective strategies to improve efficiency and promote sustainable development.

6. Discussion

This study aimed to assess countries’ efficiency in approaching SDG 12 and whether they can fulfil their commitments by 2030 if continuing with their current strategies. As the topic is recent and thus the availability of data is still limited, only a few studies contribute to this field. The objective of proposing a DEA approach in this paper was to aggregate the available set of SDG 12 indicators to provide a composite efficiency indicator for a group of 54 countries. Additionally, by employing DEA, we offer a more context-specific assessment of how efficiently each country is moving towards sustainable consumption and production, thereby contributing a fresh perspective to the discourse on global sustainability practices.

Consumption and production patterns have wide environmental and social impacts [61]. While economic growth traditionally improves people’s well-being, it has historically correlated with growing resource and energy consumption. The persistent increase in the consumption of finite resources not only adversely affects the environment but also significantly contributes to climate change. The 2030 Agenda is based on the assumption that technological progress, resulting in enhanced efficiency, can overcome the trade-off between the limit of overall volumes of consumption and the safeguard of livelihoods and human well-being. Although the alignment of these concepts and the emphasis on efficiency find favour in policy discussions, their scientific support remains insufficient. Moreover, the solution seems always to be the rearrangement of economic and social context, which actually requires endless growth in consumption.

The focus on SDG 12 is driven by the belief that it can help achieve a range of objectives outlined in the Agenda, and monitoring progress towards SDG 12 targets is critical to understand what could catalyse a transition to sustainable consumption and production. The current unsustainability of modern production and consumption systems requires a substantial transition to become sustainable [62].

The OECD’s proposed methodology, employing a unified metric, facilitates the comparison of performances across targets and against other relevant countries. This allows the identification of effective policies to accelerate progress and the adoption of corrective actions when necessary. In fact, OECD’s methodology and findings were also used for further in-depth analysis by countries. For instance, in Slovenia, the results helped identify indicators and target levels for the National Development Strategy 2030. In the Slovak Republic, the methodology was used to coordinate the National Investment Plan 2030 with SDGs and to develop an indicator framework to monitor it. Additionally, the Czech Republic used this methodology as a reference for the construction of a national SDG indicator.

However, such a methodology calculates a country’s distance from its current situation to the ideal situation pursued by SDG 12, framing this distance in the OECD context through the use of the whole group’s standard deviation. This means that there is no reference to the use of resources by countries and to their production processes on their way towards SDGs.

For this reason, this study aimed at enriching this field of research by introducing DEA into the assessment of the level of SDG achievement and the measurement of countries’ progress. The DEA is implemented to compute the relative efficiency of individual countries in fulfilling their commitment towards SDG 12 targets.

Comparing DEA results to the standardised distances calculated through the OECD methodology, it is possible to notice that the best performers’ group is different for the two analyses. Countries that result as efficient in the DEA are not always the ones who have already reached the targets for most of the SDG 12 indicators and, vice versa, countries that have minimised the distance from SDG 12 targets in different fields sometimes sacrifice efficiency.

In terms of efficiently achieving SDG 12, according to the DEA approach, the best performers are OECD members, partners, or candidates for accession. In particular, the fully efficient ones (presenting no values suggesting specific improvements of single inputs and/or outputs) are developed countries. They are the ones that can make the best use of their potential given their limiting conditions. The worst off, from the DEA perspective, are Serbia, Greece, Uruguay, and Peru. All the fully efficient units are high-income countries, according to the World Bank classification for 2022–2023. Hence, at a certain economic level, preferences in society begin to turn towards environmental issues, resulting in higher environmental sustainability of advanced economies. This result is in line with [58,59].

In any case, the number of restrictions in this study and areas for future research should be mentioned. First, this study lacks a temporal comparison—repeating the analysis for several years could provide a picture of each country’s trajectory—but data on single indicators of SDG are really poor, and DEA cannot work with missing data. Additionally, shortcomings of this approach also come from the fact that it is sensitive to the number of observations and of indicators included in the DEA, to the classification of variables as inputs and outputs, and to the possibility of setting a technical optimum.

If data become available for all the targets and for several years, the analysis can be reproduced, and the results and conclusions potentially reviewed.

However, this study aimed to enhance academic understanding of the factors subject to policy interventions for improving countries’ efficiency towards sustainable development. It acknowledges that circularity is a way to achieve sustainable consumption and production, along with other interlinked SDGs. SCP can contribute to poverty eradication and the achievement of the United Nations Millennium Development Goals. For developing countries, it offers opportunities such as the creation of new markets, green and decent jobs, as well as more efficient, welfare-generating natural resource management. It could also be an opportunity to adopt more resource-efficient, environmentally sound, and competitive technologies. For developed ones, it ensures the durability over time of production processes and the growth of well-being for their citizens, as they could count on a stable and sustainable society that takes care of people and the environment.

The results from this study provide a better understanding of how countries are progressing towards achieving SDG 12 by utilising data envelopment analysis (DEA). This approach differentiates itself from the previous studies by offering a more detailed evaluation of efficiency in sustainable consumption and production patterns. Unlike earlier research that primarily used aggregate indicators or limited DEA applications, this study employs a more granular DEA model that assesses countries’ efficiencies across a broader range of specific targets within SDG 12.

One significant contribution of this study is its comparative analysis of efficiency scores using a robust DEA framework. Previous studies, such as those by Velenturf and Purnell (2021) [30] and Lafortune et al. (2018) [33], have provided insights into sustainable practices but often lacked the depth of analysis provided by our DEA approach. Velenturf and Purnell (2021) [31] focused on principles for a circular economy, while Lafortune et al. (2018) [35] developed composite indicators for SDGs but did not delve into the detailed efficiency analysis of individual targets. Our study extends this work by applying DEA to evaluate how efficiently countries are meeting specific SDG 12 targets, such as material footprint and hazardous waste management, providing a more granular view of sustainability performance.

The study’s findings reveal that OECD countries generally exhibit higher efficiency scores compared to non-OECD countries, aligning with the previous research that highlights the role of developed economies in leading sustainability efforts [63]. However, our study goes further by providing a comparative perspective that identifies specific areas where non-OECD countries are making notable progress, such as in tourism sustainability and material footprint reduction. This distinction is crucial for understanding how different regions and economic contexts influence sustainability performance.

Moreover, the study addresses a gap identified in the prior literature by incorporating a detailed DEA framework that accounts for diverse input–output combinations. Unlike earlier studies that may have relied on limited indicators or less dynamic modelling approaches [32,37], our use of DEA allows for a more refined assessment of how well countries convert their resources into sustainable outcomes. This approach highlights inefficiencies and provides actionable insights for policy improvements.

7. Conclusions

The contribution of this study was to propose a measure of the current achievements towards sustainable consumption and production (SDG 12) by a heterogeneous group of countries. The application of DEA allowed us to assess the countries’ efficiency with regards to its targets and the potential areas of intervention.

First, the OECD’s standardised distance from the single SDG 12′s targets is calculated: it quantifies the road towards the single target values without taking into account the use of resources specifically used by each of the observed units. Then, a DEA is performed to measure the efficiency in the exploitation of national resources in the journey towards SCP.

The empirical results brought to light that developed and high-income countries are doing their best, presenting no fields where they can act to improve their performance. In general, OECD members, partners, or candidates for accession perform better that the rest of the countries, confirming the OECD’s fundamental support for the UN in ensuring the 2030 Agenda for Sustainable Development by bringing together its existing knowledge and its unique tools and experience. The areas that seemed to be interesting and fertile grounds for policy actions are the one of material footprint and of hazardous waste: the slack values for these variables are non-zero for almost all the countries in the sample and, on average, they are higher than the others, meaning that a more careful management is likely to produce some good results.

The concept dealing with the contradiction of how to meet two competing goals—socioeconomic development and preservation of the environment and scarce resources—refers to a new concept of sustainable development [36]. The formulation itself of sustainable development goals—and specifically of SDG 12—suggests that sustainability means more than just cutting emissions and reducing waste and resource exploitation, but it could be fully realised only through the lens of efficiency. The formulation of SDG 12 tries to address both the ever-increasing consumption and inefficient production by calling upon both morality and human ingenuity to safeguard ecosystems for future generations, as well as safeguard contemporary vulnerable groups.

In conclusion, promoting sustainable consumption and production (SDG 12) is fundamental for ensuring sustainable well-being at both the national and global levels. As the research indicates, sustainable well-being can only be achieved through practices that balance economic development with ecological sustainability and social equity [64]. By reducing resource consumption and environmental impact while promoting the fair distribution of benefits, SCP directly supports the capacity of ecosystems to regenerate and societies to thrive [65]. This balanced approach ensures that current needs are met without compromising the ability of future generations to achieve well-being.

This study significantly contributes to the literature on sustainable development by introducing a robust DEA framework to assess the efficiency of countries in achieving SDG 12. The detailed efficiency analysis provided by this study offers new insights into the relative performance of countries and emphasises the importance of specific sustainability targets. By comparing efficiency scores across a range of indicators and highlighting differences between OECD and non-OECD countries, the study provides a clearer understanding of how various factors impact progress towards sustainability goals.

The innovative aspect of this study lies in its application of DEA to evaluate multiple dimensions of SDG 12 targets, providing a detailed view of the countries’ performance. This approach enhances our understanding of the effectiveness of different policies and practices in promoting sustainable consumption and production. The findings underscore the need for targeted policy interventions that consider the unique contexts and challenges faced by different countries, thereby contributing to more effective and equitable progress towards global sustainability goals.

Overall, this study’s approach adds valuable insights into the sustainability context by revealing specific areas where countries excel or lag in their efforts to meet the SDG 12 targets. It provides a framework for future research to build upon and offers practical recommendations for policymakers to enhance efficiency and sustainability in their respective regions. The detailed DEA analysis presented here represents a significant advancement in the field, offering a more nuanced and actionable perspective on global sustainability efforts.