Assessing sustainable transition through fiscal policy: a regional analysis of tax incentives for taxi companies in Portugal

Assessing sustainable transition through fiscal policy: a regional analysis of tax incentives for taxi companies in Portugal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing sustainable transition through fiscal policy: a regional analysis of tax incentives for taxi companies in Portugal Fábia Fernandes, Ana Arromba Dinis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8109313/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study examines the role of fiscal policy in facilitating a sustainable transition within the Portuguese transport sector, with a particular focus on tax incentives for taxi companies. Using quantitative data from SABI and official statistics for the period 2015–2022, the research analyzes the relationship between tax benefits and financial and operational performance. Results from Mann–Whitney U and regression tests show that profitability indicators, namely net profit, operational income, and sales of services provided, significantly influence the level of tax benefits received. The findings suggest that fiscal policy plays a crucial role in fostering sustainable mobility while enhancing business performance. However, regional disparities highlight the need for more territorially adaptive fiscal frameworks. This paper, therefore, contributes to our understanding of how fiscal measures can be strategically aligned with sustainability goals to ensure equitable and effective transitions across regions. Sustainable transition Fiscal policy Tax incentives Transport sector Taxi companies Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction In Europe, sustainability and climate neutrality have emerged as central priorities in public policy, with fiscal mechanisms playing an increasingly important role in driving behavioral and technological change (Giacomelli et al. and Nabais, 2008 ). In Portugal, for example, fiscal incentives have been incorporated into national sustainability frameworks to accelerate the transition to a low-carbon economy (Fernandes, 2025 ). Transport remains a critical focus among the sectors most affected by these dynamics due to its high dependency on fossil fuels and significant contribution to greenhouse gas emissions (European Environment Agency, 2022). Considering the wide variety of factors that influence company performance, access to sustainable tax policies may be a key factor in evaluating and analyzing the performance of a given entity (Picas et al., 2021 and Bhalla et al., 2022 ). The availability of sustainable tax policies in various economic sectors raises the question of whether such policies may affect the economic performance of companies (Zarghami, 2025 ). In this way, indicators such as net income, turnover, operating income, and profit margin may reflect the impact (positive, negative or zero) provided by access to one or more sustainable measures in an economic, environmental and regional context (Huang et al., 2025 ). Technological evolution has increased access to information, allowing ideologies and thoughts that are considered incorrect in today's societies to be shaped and expressed. Individuals' knowledge of climate change and its negative impacts has led populations to recognize the importance of promoting sustainable development within companies and among citizens (Khatibi et al., 2021 ; Reyes-García et al., 2020 ). Given the inevitable need to transport goods and/or people, and considering the levels of pollution (air, noise, etc.) inherent in the transport methods used, this study focuses primarily on the transport sector, incorporating taxi companies as a vital component in the sustainable transformation of this sector. This study examines the Portuguese taxi industry as a relevant case for analysing how fiscal instruments interact with sustainability objectives at firm level. Traditionally dominated by small and medium-sized enterprises (SMEs), the taxi subsector has faced increasing pressure to decarbonize amid competition from app-based mobility platforms. Fiscal instruments such as VT exemptions are targeted mechanisms designed to support the renewal of vehicle fleets and reduce emissions while preserving business competitiveness (Fernandes et al., 2024 ). This paper seeks to answer the following research question: To what extent do fiscal incentives influence the sustainable transition of the Portuguese taxi sector? The findings aim to inform policymakers and contribute to the broader debate on the effectiveness of fiscal policy as a tool for the sustainable transition of emission-intensive industries. 2 Transport Sector The impact of gas emissions from transport, and the opportunities that society has had and still has to create and use green technologies is reflected; however, the trend is towards the use of more polluting technologies (Shah et al., 2021 and Vergragt & Brown, 2007 ). Currently, in the middle of 2024, the impacts of more polluting technologies are still discussed, and, following the focus on the transport sector, the use of combustion vehicles has been one of the main causes of air pollution (Ďaďová, 2023 ). This is shown in Fig. 1 . Transport was responsible for about 1/4 of GHG emissions in the European Union (in 2019), with the aggregate of road transport responsible for approximately 72% of these emissions as reported the European Environment Agency (2022). To make visible the evolution of GHG emissions, Table 1 presents values referring to them in a European context. Table 1 GHG emissions in the European Union, total and in the transport sector 1 Groups/Countries Total Transport Sector Years 1990 2022 1990 2022 European Union (EU27) 4,867,243 3,374,743 672,183 803,284 Germany 1,250,658 749,965 164,457 148,629 Austria 79,083 72,844 13,949 20,712 Belgium 145,849 103,576 20,930 24,187 Bulgaria 99,035 58,484 6,516 9,940 Cyprus 5,578 8,779 1,237 2,026 Croatia 32,037 25,689 3,899 6,731 Denmark 71 638 42,055 10,740 12,028 Slovakia 73,455 37,052 6,816 7,779 Slovenia 18,802 15,615 2,737 5,794 Spain 287,286 294,201 58,651 90,457 Estonia 40,274 13,952 2,571 2,645 Finland 71,331 45,700 12,091 9,777 France 539,494 395,674 121,908 127,979 Greece 103,986 78,271 14,503 17,909 Hungary 95,062 59,535 8,936 15,075 Ireland 55,231 60,605 5,143 11,751 Italy 522,373 410,289 102,190 109,431 Latvia 26,061 10,131 3,037 3,142 Lithuania 48,054 18,942 5,811 6,011 Luxembourg 12,727 8,192 2,627 4,217 Malta 2,626 2,263 351 724 Netherlands 222,704 153,384 27,796 25,046 Poland 475,724 380,509 20,741 69,333 Portugal 58,950 56,382 10,820 17,062 Czech Republic 201,314 117,688 11,249 19,391 Romania 256,647 109,715 12,433 21,148 Sweden 71,263 45,249 20,043 14,002 Iceland 3,645 4,666 616 976 Norway 51,263 48,879 9,920 12,709 Switzerland 55,058 41,630 14,673 13,581 Source : PORDATA (2024) Through a brief analysis of Table 1 , it is possible to observe that the values of GHG emissions underwent a positive evolution (i.e., there was a reduction in values) between the years 1990 and 2022. However, concerning GHG emissions in the transport sector, not all countries showed a reduction in their values. In this follow-up, Fig. 2 reflects the representativeness of the values of the transport sector in the years 1990 and 2022, compared to total GHG emissions. CO2 emissions in the transport sector have increased significantly in the several countries represented. Which is indeed a major concern, and one that should be addressed as a matter of urgency (Chapman, 2007 ; Santos, 2017 and Anable & Shaw, 2007 ). In order to reduce CO2 emissions and to achieve climate neutrality (referred to in the European Green Deal 2 ), a 90% reduction in GHGs from the transport sector will be needed by 2050 (compared to 1990) as stated by the (Comissão Europeia, 2021). Although there are several concerns surrounding this sector and its environmental impact, this is the only one where CO2 emissions have increased in the last thirty years (showing about 33.5% growth between the years 1990 and 2019) as supports the Portuguese Transport and Mobility (2024). This fact highlights the challenge behind sustainable transformation in this sector and the (urgent) need for intervention by international and national entities. In this follow-up, the European Environment Agency (2022), refers to the variation in CO2 emission values by type of (road) passenger transport and underlines cars as being the most polluting (since the maximum passenger capacity is much lower than that of buses, for example), as shows Fig. 3 . Technological evolution has led to the emergence of electric vehicles and, with the perception of the impact caused by traditional vehicles (Diesel and Gasoline), they are recognized as a sustainable alternative (Alanazi, 2023 ). However, the question is if electric vehicles are more sustainable. In 2019, most vehicles in Europe used fuels such as diesel (66.7%) or gasoline (24.55%) and, for these percentages to decrease, two solutions are mentioned (European Environment Agency, 2022): the production of more efficient and less polluting vehicles and the change in the type of fuel used (use of renewable energies). Significant increases (in a European context) in the representativeness of electric vehicles are then presented, from 10.70% (2020) to 17.80% (2021) in a period of one year. This result may have been influenced by the implementation of CO2 targets (European Green Deal). Not far away, Portugal's reality regarding vehicles in circulation by type of fuel (represented in Table 2 ) shows increases like those of the European Union. Table 2 Vehicles in circulation by type of fuel, in Portugal Fuel Type Total Diesel Petrol GPL Other 3 2010 6,182,051 3,546,659 57.37% 2,587,487 41.85% 38,982 0.63% 8,923 0.14% 2015 6,083,694 3,818,327 62.76% 2,197,118 36.11% 48,821 0.80% 19,428 0.32% 2020 7,021,112 4,586,548 65.33% 2,252,846 32.09% 59,401 0.85% 122,317 1.74% 2021 7,090,889 4,621,937 65.28% 2,230,882 31.46% 62,237 0.88% 175,833 2.48% 2022 7,249,033 4,686,198 64.65% 2,250,747 31.05% 67,677 0.93% 244,411 3.37% Source: PORDATA, 2024 As can be seen, the trend of the type of fuel is skewed towards combustion vehicles, highlighting the aggregate of diesel vehicles, which, over the years (2010, 2015, 2020, 2021, and 2022) represent more than 1/2 of the total vehicles in circulation. Concerning the set of gasoline vehicles, it presents lower values than diesel vehicles; however, its representativeness is quite high compared to the "LPG" and "Others" aggregates. In 2010, the total number of vehicles on the road was approximately 6,18 million, of which 57.37% were diesel-powered and 41.85% were gasoline-powered, reflecting the predominance of traditional internal combustion engines. The weight of LPG vehicles and other categories was residual, representing only 0.63% and 0.14%, respectively. Five years later, in 2015, there was an increase in the share of diesel vehicles, which rose to 62.76%, while gasoline vehicles fell to 36.11%. In 2020, the total number of vehicles reached 7,02 million, representing growth of approximately 15.4% 4 compared to 2015. Diesel continues to dominate the market, commanding 65.33% of the total, though its growth rate has exhibited a modest decline in recent years. In contrast, the "Others" categories (which principally incorporate electric and hybrid vehicles) will witness a substantial increase, rising from 0.32% in 2015 to 1.74% in 2020. In recent years, specifically in 2021 and 2022, there has been a stabilization in the share of diesel vehicles, which remained at approximately 65% in 2021 and 64.65% in 2022. Concurrently, the percentage of gasoline vehicles continued to decline marginally to 31.05%. The most significant growth is observed in the "other" fuel sources category, which increased to 3.37% in 2022, more than doubling compared to 2020. This increase signifies a progressive entry of electric and hybrid vehicles into the market, driven by environmental policies, tax incentives, and growing public awareness of climate change. In summary, the period from 2010 to 2022 is characterized by a phase of consolidation of diesel as the dominant fuel, followed by the beginning of energy diversification, especially after 2020 (when the percentage reached values above 1%). If this trend continues, it is expected that, in the next decade, alternative energy vehicles will represent an increasingly significant fraction of the national vehicle fleet. This reflects the structural changes underway in the transport sector and in European energy policy. In summary, Fig. 4 presents the evolution of values (in percentage) of the various sets, in the period from 2010 to 2022. It is thus possible to observe that all sets, except the aggregate of gasoline vehicles, suffered an increase in this period: diesel vehicles - increased by about 7.30%; gasoline vehicles: decreased by about 11%; LPG vehicles- increased by about 0.30%.; "Others" category - increased about 3.20%. Assuming the "Other" category as a representative of sustainable vehicles, its positive evolution is notorious. Although the production and disposal of electric vehicles is less eco-friendly compared to traditional vehicles, not only CO2 emissions in their production should be taken into account, but also the use of the vehicle and, subsequently, its disposal/scrapping (European Environment Agency., 2022 and Panthi, 2011 ). Taking into account the above, electric vehicles have proven to be more sustainable means of transport than combustion vehicles and, in the future (Borén, 2016 ), the use of renewable energies (more pronounced) for electricity production will reduce their impact (at least in terms of fuel type) and, through the European Union's plans to produce more sustainable batteries, they will become less harmful to the environment (Directive 2005/36/EC of the European Parliament and of the Council of 7 September 2005, 2015). 3 Taxi Companies The introduction of sustainable transport solutions in urban areas represents a cornerstone of the European Union’s broader sustainability agenda. Within this framework, the transition of taxi companies towards environmentally friendly vehicle fleets plays a crucial role in the decarbonization of the mobility sector. However, the high acquisition costs of sustainable vehicles continue to be a barrier for both private citizens and companies, particularly small and medium-sized enterprises (SMEs) operating in the taxi industry (Kövesdi, 2023 ). Innovation and the adoption of green technologies within taxi services are recognized as essential steps toward transforming public transport systems (Hofer, 2014 ). Taxi companies not only provide critical last-mile connectivity but also act as visible representatives of sustainable mobility in cities 5 (Hassouna & Assad, 2020 ). The economic and environmental impact of these firms thus extends beyond their own operations, influencing broader societal perceptions of sustainable transport. In Portugal, the relevance of taxi companies is reflected in their number and geographical distribution, which make them a key component of the national mobility ecosystem. As shown in Table 3 , the number of taxi operators has fluctuated over recent years, largely influenced by economic cycles, digital platform competition, and regulatory changes. Table 3 Evolution of taxi companies in Portugal Info/Year 2016 2017 2018 2019 2020 2021 2022 Taxi Number of conductors 24,684 28,892 25,699 26,384 26,984 21,902 20,224 Number of companies 21 25 29 35 37 41 41 Ride-hailing service Ride-hailing operators – – – 6,913 8,214 9,175 12,455 E-platform operators – – – 8 8 11 13 Source: Portuguese Institute for Mobility and Transport 6 (IMT, 2024) The data reveal two important trends: first, the overall stability of traditional taxi firms despite the competitive expansion of ride-hailing services (e.g., Uber, Bolt, and Free Now); and second, the increasing diversification of the sector through digitalization and hybrid operational models. The number of taxi companies has remained relatively constant since 2020, while the rise of app-based mobility platforms has introduced new business models that coexist—sometimes competitively—with conventional taxi services. In Portugal, Article 53 of the Portuguese Vehicle Tax Code (VTC) establishes a tax benefit designed to support taxi companies in adopting cleaner technologies. Law No. 2/2020 of 31 March grants a 70% exemption on vehicle tax (VT) for passenger and mixed-use vehicles assigned to taxi or rental-with-driver services (vehicles with plates “A” or “T”). This exemption applies to vehicles up to four years old, provided their CO₂ emissions remain below 160 g/km (NEDC) or 184 g/km (WLTP). Vehicles equipped exclusively with natural gas, electricity, or hybrid engines are fully exempt from VT (paragraph 2 of Article 53, VTC). To access this benefit, companies must be registered with the Directorate-General of Customs and Excise Duties and submit a specific application form (Form 7 under Article 53, VTC). These fiscal measures aim to accelerate the decarbonization of the taxi fleet by reducing the relative cost of cleaner vehicles. However, the extent to which companies benefit from such incentives depends on their financial stability, size, and regional context, as support Fernandes et al. ( 2024 ) and Weber & Rohracher ( 2012 ). Smaller operators often face difficulties meeting administrative requirements or accessing vehicles that comply with emission thresholds, particularly outside metropolitan regions. Therefore, understanding the regional and structural determinants of fiscal benefit uptake among taxi companies is crucial to evaluating the overall effectiveness of sustainable transport policy in Portugal. To answer this, the study compares taxi companies that accessed the VT tax benefit (beneficiary entities) with those that did not. The empirical investigation evaluates whether economic and financial characteristics—such as profitability, capital structure, and operational costs—determine access to fiscal incentives for the acquisition of environmentally friendly vehicles. The results of this analysis are presented in the next section. 4 What is the regional impact of tax incentives on taxi companies? The empirical analysis was carried out using data from the SABI 7 database, comprising approximately 14,900 registered taxi companies operating in Portugal. Due to the extensive size of the data set, a representative sample of 630 firms was selected to ensure statistical robustness while maintaining sectoral diversity (315 from Group A and 315 from Group B) 8 . The analysis focuses on identifying whether access to the VT tax benefit significantly influences the financial and operational performance of these companies during the period 2015–2022. A regional analysis was carried out on the sample (Table 4 ) Table 4 Sample´s regional analysis Number of taxi companies Region Number 9 Percentage (%) Alentejo 24 3.83 Algarve 28 4.47 Centro 30 4.78 Lisboa e Vale do Tejo 320 51.4 Norte 224 35.73 Região Autónoma da Madeira 4 0.64 Região Autónoma dos Açores 0 0 Total 630 100 The analysis by region clearly shows that most Portuguese companies took advantage of the tax benefit in question are from Lisboa e Vale do Tejo (51.4%). It is also evident that within the identified regions, the northern region is near Lisbon. However, the sum of the remaining regions does not reach even 14% of the total beneficiary companies in the period under review. This reveals significant concern regarding the definition of the tax policy in question, regarding regional disparities. However, to provide a comprehensive analysis, it is necessary to consider other factors that are relevant to this study. Five groups of variables were defined, each representing a specific financial or structural dimension of company performance: (i) profitability indicators (net profit, turnover, operational income, profit margin); (ii) capital efficiency (return on capital invested, solvency ratio); (iii) equity-related measures (equity per employee, equity ratio); (iv) cost-related indicators (average cost per employee); and (v) operational variables (transport and sales services rendered). These variables were examined to test the following hypotheses (H1–H5): H1: The averages of net profit, turnover, operational income, sales services rendered, and sales services provided determine access to the VT tax benefit. H2: The averages of return on capital invested and profit margin, as profitability variables, influence the likelihood of accessing the VT tax benefit. H3: The solvency ratio average, as a structural variable, affects access to the VT tax benefit. H4: The averages of equity per employee and average cost per employee determine access to the VT tax benefit. H5: The transport average determines access to the VT tax benefit. Group analysis An Independent-Samples Mann–Whitney U Test was conducted to compare the distribution of each variable across two categories of Total Tax Benefit (TBF): beneficiary and non-beneficiary companies. The test was chosen because it does not assume normal distribution of data and is suitable for comparing independent samples of unequal sizes. Table 5 summarizes the hypothesis testing outcomes. Variables with p-values below 0.05 indicate statistically significant differences between the two groups. Table 5 Hypothesis Test Summary (Mann-Whitney U Test) 1 Null Hypothesis Test Sig. a,b Decision The distribution of return _on _capital _invested average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test <,001 Reject the null hypothesis. 2 The distribution of solvency_ratio_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test ,081 Retain the null hypothesis. 3 The distribution of equity_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test ,661 Retain the null hypothesis. 4 The distribution of avacost_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test <,001 Reject the null hypothesis. 5 The distribution of salesRE_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test ,419 Retain the null hypothesis. 6 The distribution of salesPROV_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test ,203 Retain the null hypothesis. 7 The distribution of transp_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test ,003 Reject the null hypothesis. 8 The distribution of net_profit_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test <,001 Reject the null hypothesis. 9 The distribution of turnover_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test ,160 Retain the null hypothesis. 10 The distribution of operacional_inc_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test ,226 Retain the null hypothesis. 11 The distribution of profit_margin_average is the same across categories of TBF. Independent-Samples Mann-Whitney U Test ,291 Retain the null hypothesis. a. The significance level is ,050. b. Asymptotic significance is displayed. The test results, summarized in Table 5 , reveal statistically significant differences (p < 0.05) for four variables: return on capital invested, average cost per employee, transport average, and net profit average. Specifically, companies that benefited from tax incentives demonstrated higher capital efficiency, profitability, and transport capacity, but lower average employee costs. These results suggest that beneficiary companies tend to manage resources more efficiently and operate at greater scale compared to non-beneficiaries. Conversely, the solvency ratio, equity average, sales services rendered, sales services provided, turnover, operational income, and profit margin showed no significant differences (p > 0.05), indicating that these variables do not independently influence access to fiscal incentives. This suggests that while profitability and operational activity are linked to benefit uptake, structural stability and sales volume alone are not sufficient determinants. Correlation analysis A Spearman correlation analysis was performed to identify relationships between total tax benefits (2015–2022) and the financial indicators for Group A (beneficiary companies). Table 6 Spearman correlation coefficients 10 (Group A) Group A total_tax_benefits_2015_2022 total_tax_benefits_2015_2022 Spearman correlation 1 Sig. - return _on _capital _invested Spearman correlation 0.223** Sig. < 0.001 solvency_ratio Spearman correlation -0.015 Sig. 0.790 equity_per_employee Spearman correlation 0.072 Sig. 0.201 average_cost_per_employee Spearman correlation -0.071 Sig. 0.209 sales_services_rendered_equity Spearman correlation 0.035 Sig. 0.536 sales_services_provided Spearman correlation 0.262** Sig. < 0.001 transport Spearman correlation 0.140* Sig. 0.013 net_profit Spearman correlation 0.288** Sig. < 0.001 turnover Spearman correlation 0.243** Sig. < 0.001 operacional_income Spearman correlation 0.246** Sig. < 0.001 profit_margin Spearman correlation -0.052 Sig. 0.354 Notes : Spearman correlations are significant at **the 0.01 level (2-tailed) and at *the 0.05 level (2-tailed) The results indicate several positive and statistically significant correlations: net profit (ρ = 0.288*, p < 0.01), sales services provided (ρ = 0.262**, p < 0.01), operational income (ρ = 0.246**, p < 0.01), turnover (ρ = 0.243**, p < 0.01), return on capital invested (ρ = 0.223**, p < 0.01), and transport (ρ = 0.140*, p < 0.05). These findings demonstrate that companies with stronger financial performance and higher levels of operational activity are more likely to benefit from tax incentives. This relationship reflects the dual purpose of fiscal policy in promoting both sustainability and competitiveness within the sector. By contrast, the solvency ratio (ρ = −0.015), equity per employee (ρ = 0.072), average cost per employee (ρ = −0.071), sales services rendered (ρ = 0.035), and profit margin (ρ = −0.052) exhibited weak or non-significant correlations. Thus, factors such as capital structure and labor cost intensity appear less relevant in determining tax benefit outcomes. Overall, correlation analysis supports the hypothesis that profitability and operational efficiency are the main predictors of access to fiscal benefits, while cost structure and solvency play only a marginal role. Causal model Comparing taxi companies that benefited from tax incentives for less polluting cars (Group A: a database of 237 taxi companies) with others in the same sector that did not (Group B: database of 6,773 taxi companies, including Group A companies), the ANOVA results show that there are differences between companies that did and did not take advantage of the tax benefits under investigation, so the authors applied the regression model to Group A only. Multiple linear regression was used to verify whether access to environmental tax benefits for less polluting cars can be predicted by taxi companies´ economic and financial features, i.e., the authors applied a model to explain which characteristics of taxi companies are significant in inducing access to environmental tax incentives under study. The dependent variable is Taxb [average tax benefits for less polluting cars]. The independent variables are: Opres (operational result), NetProf (net profit) , Tasset (total assets), Conprf, Emplnb , Turn (turnover) , Perf (performance) , GVA (Gross Value Added), Solv (solvency ratio), Transeq (transport equipment), IncTax (income tax), eqRat, Fage, ProfMG (profit margin), Debt . U i is the error term, relative to unknown factors. The mathematical specification of the regression model is: Taxb i = B 0 + B 1 Opres 1 i + B 2 NetProf 2 i + B 3 Tasset3 i + B 4 e Conprf 4 i + B 5 Emplnb 5 i + B 6 Turn 6 i + B7 Perf 7 i + B 8 GVA 8 i + B 9 Solv 9 i+ B 10 Transeq 10 i + B 11 IncTax 11 i + B 12 eqRat1 2 i + B13 Fage 13 i + B 14 ProfMG 14 i + B 15 Debt 15 i + U i (1) The dependent variable is the total amount of tax benefits obtained between 2015 and 2022, while the independent variables are sales services provided, profit margin, equity per employee, average cost per employee, and net profit. Table 7 Causal Model Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 0.706 a 0.498 0.490 2223.56250 0.498 61.420 5 309 < 0.001 a. Predictors: (Constant), salesPROV_mean, profit_margin_mean, equity_mean, avacost_mean, net_profit_mean b. Dependent Variable: total_tax_benefits_2015_2022 The model produced a strong fit (R = 0.706; R² = 0.498; Adjusted R² = 0.490), indicating that approximately 49.8% of the variance in total tax benefits can be explained by the selected predictors. The model’s F-statistic (F = 61.42, p < 0.001) confirms that the regression equation is statistically significant, meaning that the independent variables collectively provide a robust explanation for differences in tax benefit allocation among taxi companies. Table 8 Regression Results Coefficients Model Unstandardized Coefficients t Sig. B S.E. (Constant) 3978,752 312,296 12,740 < 0.001 transp_mean -0,701 14,127 -0,050 0.960 net_profit_mean*** -114,683 19,993 -5,736 < 0.001 profit_margin_mean** -0,002 0,001 -2,200 0.029 retcapinv_mean 0,350 0,387 0,903 0.367 solvrat_mean -0,493 1,201 -0,410 0.682 equity_mean** 15,229 6,341 2,402 0.017 avacost_mean*** -231,962 35,600 -6,516 < 0.001 salesRE_mean -0,068 0,059 -1,153 0.250 salesPROV_mean*** 32,769 1,953 16,776 < 0.001 Adjusted R 2 49.9% The analysis of coefficients (Table 8 ) reveals the following significant effects: Net profit average (B = − 114.683, p < 0.001): negative and significant, suggesting that higher profits are associated with reduced fiscal benefits—possibly reflecting eligibility limits or diminishing marginal benefits for more profitable firms. Average cost per employee (B = − 231.962, p < 0.001): negative and significant, indicating that companies with higher labor costs tend to obtain lower tax advantages. Profit margin (B = − 0.002, p = 0.029): small but significant negative effect, showing that higher profitability margins slightly decrease benefit access. Equity per employee (B = 15.229, p = 0.017): positive and significant, meaning firms with greater capitalization per employee are more likely to secure higher benefits. Sales services provided (B = 32.769, p < 0.001): positive and highly significant, confirming that greater business activity and service output increase the probability and scale of fiscal benefits received. The regression results reveal a dual fiscal–economic dynamic: while tax benefits encourage investment in sustainable transport, they are more accessible to financially healthier firms, potentially exacerbating regional and structural disparities. Firms with greater equity and stronger service performance receive proportionally higher tax benefits, whereas smaller firms with higher costs and limited profits benefit less. This pattern underlines the necessity of designing fiscal mechanisms that not only incentivize sustainability but also ensure equitable access across different company profiles. In essence, the analysis confirms that fiscal policy can effectively shape sustainable transformation when it is complemented by measures that account for firm level and regional heterogeneity. 5 Discussion The results of the statistical analysis suggest a dual relationship between financial performance and fiscal incentives. On one hand, the provision of tax benefits clearly promotes sustainable investment within the sector. On the other hand, the accessibility of these benefits appears to be conditioned by firms’ pre-existing financial strength. This creates a paradox where economically robust firms benefit more, potentially widening regional and structural disparities. The data also reveal that operational efficiency—captured by sales service activity and equity intensity—is a strong determinant of fiscal benefit access. Smaller companies with limited resources face higher barriers, such as compliance costs or credit constraints, which may prevent them from adopting sustainable technologies despite the existence of tax incentives. These results highlight the importance of aligning fiscal policy design with regional and structural realities. Policymakers should consider the introduction of complementary measures—such as targeted subsidies, simplified procedures for small firms, or technical assistance programs—to ensure that fiscal incentives contribute not only to environmental goals but also to social and territorial cohesion. The findings confirm the critical role of fiscal instruments in facilitating sustainable transitions, particularly in high-emission sectors such as transport. In Portugal, targeted tax incentives for taxi companies shows how fiscal mechanisms can integrate environmental and economic goals simultaneously. Nonetheless, the persistence of regional disparities in the uptake of benefits reveals that fiscal frameworks must become more territorially adaptive and context sensitive. From a policy design perspective, this implies that national governments should incorporate regional specificities into fiscal planning, considering market maturity, fleet characteristics, and infrastructure availability. Uniform policies may unintentionally reproduce inequalities between metropolitan and peripheral areas. Therefore, adaptive fiscal instruments—coordinated with local authorities—are essential to promote a fair and balanced transition. These results align with contemporary research on sustainable transitions, which argues for multi-level governance and adaptive policymaking. Fiscal policies, while effective in encouraging the adoption of cleaner technologies, must be supported by complementary tools such as low-interest financing, simplified administrative procedures, and investments in charging networks. Integrating these mechanisms would ensure that fiscal incentives deliver both environmental and socioeconomic benefits. Overall, the study underscores the potential of fiscal policies to function not only as revenue instruments but also as levers for systemic change. To achieve this, public decision-makers should combine tax design with spatial analysis, monitoring territorial outcomes to ensure that policies contribute to cohesive and inclusive sustainability transitions. 6 Conclusions This study shows that fiscal incentives are powerful instruments for steering the transport sector toward sustainability. Profitability and cost-efficiency indicators show significant positive correlations with tax benefits, indicating that economically resilient companies are better positioned to engage in sustainable transitions. In contrast, higher average costs per employee and profit margins are associated with reduced access to benefits, reflecting the complexity of tax–performance dynamics. The results confirm that well-designed fiscal instruments can effectively balance environmental objectives with business competitiveness. However, to maximize their impact, fiscal policies must integrate territorial differentiation. Local socioeconomic structures, access to electric infrastructure, and company size all shape the ability of firms to benefit from tax incentives. Accordingly, policymakers should adopt a multi-level, data-driven approach to fiscal governance. Integrating spatial and performance data into fiscal planning would allow more equitable access to tax benefits, strengthening both environmental and social cohesion. The Portuguese case provides an example of how fiscal tools can foster transformative change when aligned with broader sustainable development strategies. In line with the European Green Deal, these findings highlight that achieving climate neutrality by 2050 requires fiscal systems that reward sustainable investment and penalize environmentally harmful practices. By embedding territorial analysis into fiscal design, public decision-makers can ensure that the green transition becomes not only sustainable but also just. Declarations Author Contribution F.F and A.D wrote the main manuscript text and prepared all the figures . All authors reviewed the manuscript. Acknowledgement This research was supported by national funds through the FCT – Portuguese Foundation for Science and Technology, I.P., by the project reference 2023.12454.PEX and DOI identifier: https://doi.org/10.54499/2023.12454.PEX. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Giacomelli M, Görgün EK, Salata S, Ronchi S, Bernardini C, Costa MM, Arcidiacono A, Concilio G (2025) Climate neutrality and urban planning: A state of the art from literature and the European cities. In Sustainable Cities and Society (Vol. 130). Elsevier Ltd. https://doi.org/10.1016/j.scs.2025.106570 Nabais JC (2008) Tributos com fins ambientais. Rev Trib Finanç Públicas 16(80):253–283 Fernandes F (2025) Sustainable Mobility in Action. In Corporate Climate Responsibility and Education (pp. 375–406). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-1937-7.ch011 European Environment Agency (EEA) (2022) Greenhouse gas emissions from transport in Europe. EEA Report No. 23/2022. Copenhagen: EEA. https://www.eea.europa.eu/publications/greenhouse-gas-emissions-from-transport Picas S, Reis P, Pinto A, Abrantes JL (2021) Does tax, financial, and government incentives impact long-term Portuguese SMEs’ sustainable company performance? Sustainability 13(21):11866. https://doi.org/10.3390/su132111866 Bhalla N, Kaur I, Sharma RK (2022) Examining the effect of tax reform determinants, firms’ characteristics and demographic factors on the financial performance of small and micro enterprises. Sustainability 14(14):8270. https://doi.org/10.3390/su14148270 Zarghami SA (2025) The role of economic policies in achieving sustainable development goal 7: Insights from OECD and European countries. Applied Energy , 377 . https://doi.org/10.1016/j.apenergy.2024.124558 Huang C, Hao S, Ma L (2025) The impact of ESG advantages on the economic development of neighboring regions. Energy Economics , 145 . https://doi.org/10.1016/j.eneco.2025.108431 Huang C, Hao S, Ma L (2025) The impact of ESG advantages on the economic development of neighboring regions. Energy Economics , 145 . https://doi.org/10.1016/j.eneco.2025.108431 Khatibi FS, Dedekorkut-Howes A, Howes M, Torabi E (2021) Can public awareness, knowledge and engagement improve climate change adaptation policies? In Discover Sustainability (Vol. 2, Issue 1). Springer Nature. https://doi.org/10.1007/s43621-021-00024-z Fernandes F, Dinis A, Pereira L, Carvalho A (2024) Políticas fiscais sustentáveis nas empresas táxis: proposta de modelo de avaliação do benefício fiscal em sede de ISV. Rev Juríd Portucalense 36:426–449. https://doi.org/10.34625/issn.2183-2705(36)2024.ic-19 Shah KJ, Pan SY, Lee I, Kim H, You Z, Zheng JM, Chiang PC (2021) Green transportation for sustainability: review of current barriers, strategies, and innovative technologies. J Clean Prod 326:129392. https://doi.org/10.1016/j.jclepro.2021.129392 Vergragt PJ, Brown HS (2007) Sustainable mobility: from technological innovation to societal learning. J Clean Prod 15(11):1104–1115. https://doi.org/10.1016/j.jclepro.2006.05.020 Ďaďová K (2023) Emissions from road transport and implications for sustainable mobility in Europe. Transp Res Part D: Transp Environ 117:103656. https://doi.org/10.1016/j.trd.2023.103656 Chapman L (2007) Transport and climate change: a review. J Transp Geogr 15(5):354–367. https://doi.org/10.1016/j.jtrangeo.2006.11.008 Santos G (2017) Road transport and CO2 emissions: what are the challenges? Transp Policy 59:71–74. https://doi.org/10.1016/j.tranpol.2017.06.007 Anable J, Shaw J (2007) Priorities, policies and (time)scales: the delivery of emissions reductions in the UK transport sector. Area 39(4):443–457. https://doi.org/10.1111/j.1475-4762.2007.00776.x Comissão E (2021) Objetivo 55: alcançar a meta climática da UE para 2030 rumo à neutralidade climática . https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal/delivering-european-green-deal/fit-55_en Alanazi A (2023) Electric vehicles and sustainable urban transport transitions: Challenges and opportunities. Energy Policy 175:113456. https://doi.org/10.1016/j.enpol.2023.113456 Panthi L (2011) Carbon footprint and environmental documentation of product—a case analysis on road construction. Master thesis, Institutt for industriell økonomi og teknologiledelse, NTNU. https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/234555 Borén S (2016) Sustainable personal road transport: the role of electric vehicles. Blekinge Institute of Technology, Karlskrona. https://urn.kb.se/resolve?urn=urn:nbn:se:bth-11715 DIRECTIVE 2006/66/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL, Official Journal of the European Union (2006) https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32006L0066 Kövesdi I (2023) Greener vehicle taxation in Hungary. Transp Res Procedia 72:4207–4214. https://doi.org/10.1016/j.trpro.2023.11.353 Hofer J (2014) Sustainability assessment of passenger vehicles: analysis of past trends and future impacts of electric powertrains. ETH Zurich. https://doi.org/10.3929/ETHZ-A-010252775 Hassouna H, Assad R (2020) Innovation in taxi operations: Pathways to sustainable urban mobility. J Clean Prod 259:120919. https://doi.org/10.1016/j.jclepro.2020.120919 Weber KM, Rohracher H (2012) Legitimising research, technology and innovation policies for transformative change: Combining insights from innovation systems and the multi-level perspective in a comprehensive ‘failures’ framework. Res Policy 41(6):1037–1047. https://doi.org/10.1016/j.respol.2011.10.015 Reyes-García V, Fernández-Llamazares Á, García-del-Amo D, Cabeza M (2020) Operationalizing Local Ecological Knowledge in Climate Change Research: Challenges and Opportunities of Citizen Science. In: Welch-Devine M, Sourdril A, Burke B (eds) Changing Climate, Changing Worlds. Ethnobiology. Springer, Cham. https://doi.org/10.1007/978-3-030-37312-2_9 Fernandes F, Dinis A, Pereira L, Carvalho A (2024) Sustainable tax policy for taxi companies: a proposal for an evaluation model of Vehicle Tax Incentive. Revista Jurídica Portucalense 426–449. https://doi.org/10.34625/issn.2183-2705(36)2024.ic-19 Footnotes t CO2eq = ton of carbon dioxide equivalent European Green Deal: https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal_en The "Other" category aggregates the subcategories "Biodiesel, Pure electric, Plug-in hybrid electric, non-plug-in hybrid electric, and Other". Rate of change = [(Vf - Vi)/Vi]*100 Considering taxi companies as an integral part of the public transport sector. https://www.imt-ip.pt/ SABI database: https://login.bvdinfo.com/R1/SabiNeo?SetLanguage=pt Group A refers to taxi companies that have taken advantage of the tax benefit outlined in Article 53 of the VTC. Consequently, Group B refers to companies that have not accessed this tax benefit. The information was obtained from the SABI database and the Portuguese Finance Portal ( https://www.portaldasfinancas.gov.pt/pt/home.action ). It is important to note that the number of companies per region shown in Table 4 is the total number of companies in Groups A and B combined (50/50). The Spearman correlation measures the strength and direction of the monotonic relationship between two variables, with values ranging from − 1 to 1. Positive values indicate a direct relationship, meaning that as one variable increases, the other tends to increase as well, while negative values indicate an inverse relationship, where an increase in one variable is associated with a decrease in the other. The p-values indicate the significance of the correlation. Significant correlations at α = 0.01 are marked as **, while significant correlations at α = 0.05 are marked as *. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Feb, 2026 Editor assigned by journal 18 Nov, 2025 Submission checks completed at journal 18 Nov, 2025 First submitted to journal 13 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8109313","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585104005,"identity":"169a916b-c3a0-41e3-9518-1a5ae26ccdc8","order_by":0,"name":"Fábia Fernandes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBAC9gbGBxDWAQbGBwnEaOE5wGwA08JsANHCTLwWNgkGorSIHWZ8XLnDLo/vePuzioc77OQZpPsP4NcincxsePZMcrHkmTNmNxLPJBs2yBzGb4u9dP4xycY25sQNN3LYbiS2HWBskEgm4DDpZPafjW31iRvuP39WANRiT4wWNsbGtsNAWxjMGIBaEonRwgx02PHEmWdyjCWAfklukzlsQEgL48fGturEvuPHH378ucPOtl+68QF+a1AAYwMDPHZI0MJAmpZRMApGwSgYAQAAMHJJw11S110AAAAASUVORK5CYII=","orcid":"","institution":"Polytechnic Institute of Cávado and Ave","correspondingAuthor":true,"prefix":"","firstName":"Fábia","middleName":"","lastName":"Fernandes","suffix":""},{"id":585104006,"identity":"55d10cbe-744a-4e5a-bc27-97072c2039ec","order_by":1,"name":"Ana Arromba Dinis","email":"","orcid":"","institution":"Polytechnic Institute of Cávado and Ave","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Arromba","lastName":"Dinis","suffix":""}],"badges":[],"createdAt":"2025-11-13 23:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8109313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8109313/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104958314,"identity":"fb5159f6-011d-4ac8-a351-e18ab1b322ec","added_by":"auto","created_at":"2026-03-19 08:27:53","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45411,"visible":true,"origin":"","legend":"\u003cp\u003eBreakdown of GHG Emissions, by mode of transport.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEuropean Environment Agency \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(2022)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8109313/v1/baee7db9083aadcb16abfd5a.jpeg"},{"id":104958359,"identity":"e2cac5cf-d2ba-4fa3-b7e6-f3568017dfe1","added_by":"auto","created_at":"2026-03-19 08:27:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78779,"visible":true,"origin":"","legend":"\u003cp\u003eGraph: Representativeness of GHG Emissions from the Transport Sector, \u003cstrong\u003eSource: PORDATA, 2024\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8109313/v1/04b195842f19db12f3fc8633.jpeg"},{"id":104958394,"identity":"b72b5a2a-670e-4185-aacc-2ee44604e721","added_by":"auto","created_at":"2026-03-19 08:28:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30990,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of the percentage of CO2 emissions by number of passengers\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8109313/v1/71bde9b559f5af9d54fdd5a1.jpeg"},{"id":104958361,"identity":"6f9bd086-44dc-4031-b4bd-b4749eb43abf","added_by":"auto","created_at":"2026-03-19 08:27:59","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71030,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of the Representativeness of Vehicles in circulation by type of fuel, in Portugal\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8109313/v1/9b436f697396b46d26e403ed.jpeg"},{"id":104958410,"identity":"c6d1d703-e598-4159-b0af-1937da8075d9","added_by":"auto","created_at":"2026-03-19 08:28:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1383294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8109313/v1/535f9e30-07bd-47c6-a5b0-e7f82c50d1e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing sustainable transition through fiscal policy: a regional analysis of tax incentives for taxi companies in Portugal","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn Europe, sustainability and climate neutrality have emerged as central priorities in public policy, with fiscal mechanisms playing an increasingly important role in driving behavioral and technological change (Giacomelli et al. and Nabais, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In Portugal, for example, fiscal incentives have been incorporated into national sustainability frameworks to accelerate the transition to a low-carbon economy (Fernandes, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Transport remains a critical focus among the sectors most affected by these dynamics due to its high dependency on fossil fuels and significant contribution to greenhouse gas emissions (European Environment Agency, 2022).\u003c/p\u003e \u003cp\u003eConsidering the wide variety of factors that influence company performance, access to sustainable tax policies may be a key factor in evaluating and analyzing the performance of a given entity (Picas et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e and Bhalla et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe availability of sustainable tax policies in various economic sectors raises the question of whether such policies may affect the economic performance of companies (Zarghami, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this way, indicators such as net income, turnover, operating income, and profit margin may reflect the impact (positive, negative or zero) provided by access to one or more sustainable measures in an economic, environmental and regional context (Huang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTechnological evolution has increased access to information, allowing ideologies and thoughts that are considered incorrect in today's societies to be shaped and expressed. Individuals' knowledge of climate change and its negative impacts has led populations to recognize the importance of promoting sustainable development within companies and among citizens (Khatibi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reyes-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the inevitable need to transport goods and/or people, and considering the levels of pollution (air, noise, etc.) inherent in the transport methods used, this study focuses primarily on the transport sector, incorporating taxi companies as a vital component in the sustainable transformation of this sector.\u003c/p\u003e \u003cp\u003eThis study examines the Portuguese taxi industry as a relevant case for analysing how fiscal instruments interact with sustainability objectives at firm level. Traditionally dominated by small and medium-sized enterprises (SMEs), the taxi subsector has faced increasing pressure to decarbonize amid competition from app-based mobility platforms. Fiscal instruments such as VT exemptions are targeted mechanisms designed to support the renewal of vehicle fleets and reduce emissions while preserving business competitiveness (Fernandes et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper seeks to answer the following research question: To what extent do fiscal incentives influence the sustainable transition of the Portuguese taxi sector? The findings aim to inform policymakers and contribute to the broader debate on the effectiveness of fiscal policy as a tool for the sustainable transition of emission-intensive industries.\u003c/p\u003e"},{"header":"2 Transport Sector","content":"\u003cp\u003eThe impact of gas emissions from transport, and the opportunities that society has had and still has to create and use green technologies is reflected; however, the trend is towards the use of more polluting technologies (Shah et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e and Vergragt \u0026amp; Brown, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Currently, in the middle of 2024, the impacts of more polluting technologies are still discussed, and, following the focus on the transport sector, the use of combustion vehicles has been one of the main causes of air pollution (Ďaďov\u0026aacute;, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTransport was responsible for about 1/4 of GHG emissions in the European Union (in 2019), with the aggregate of road transport responsible for approximately 72% of these emissions as reported the European Environment Agency (2022).\u003c/p\u003e \u003cp\u003eTo make visible the evolution of GHG emissions, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents values referring to them in a European context.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGHG emissions in the European Union, total and in the transport sector\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroups/Countries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTransport Sector\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1990\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1990\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean Union (EU27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,867,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,374,743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e672,183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e803,284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,250,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e749,965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e164,457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e148,629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20,712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145,849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103,576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24,187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99,035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58,484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyprus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCroatia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25,689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlovakia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73,455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlovenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e294,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58,651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90,457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e539,494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e395,674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121,908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e127,979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103,986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHungary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59,535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e522,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e410,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109,431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatvia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLithuania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuxembourg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222,704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153,384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25,046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e475,724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e380,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69,333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56,382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCzech Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201,314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117,688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256,647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109,715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21,148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14,002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIceland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48,879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41,630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: PORDATA (2024)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThrough a brief analysis of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it is possible to observe that the values of GHG emissions underwent a positive evolution (i.e., there was a reduction in values) between the years 1990 and 2022. However, concerning GHG emissions in the transport sector, not all countries showed a reduction in their values. In this follow-up, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reflects the representativeness of the values of the transport sector in the years 1990 and 2022, compared to total GHG emissions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCO2 emissions in the transport sector have increased significantly in the several countries represented. Which is indeed a major concern, and one that should be addressed as a matter of urgency (Chapman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Santos, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e and Anable \u0026amp; Shaw, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to reduce CO2 emissions and to achieve climate neutrality (referred to in the European Green Deal\u003csup\u003e2\u003c/sup\u003e), a 90% reduction in GHGs from the transport sector will be needed by 2050 (compared to 1990) as stated by the (Comiss\u0026atilde;o Europeia, 2021).\u003c/p\u003e \u003cp\u003eAlthough there are several concerns surrounding this sector and its environmental impact, this is the only one where CO2 emissions have increased in the last thirty years (showing about 33.5% growth between the years 1990 and 2019) as supports the Portuguese Transport and Mobility (2024). This fact highlights the challenge behind sustainable transformation in this sector and the (urgent) need for intervention by international and national entities.\u003c/p\u003e \u003cp\u003eIn this follow-up, the European Environment Agency (2022), refers to the variation in CO2 emission values by type of (road) passenger transport and underlines cars as being the most polluting (since the maximum passenger capacity is much lower than that of buses, for example), as shows Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTechnological evolution has led to the emergence of electric vehicles and, with the perception of the impact caused by traditional vehicles (Diesel and Gasoline), they are recognized as a sustainable alternative (Alanazi, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the question is if electric vehicles are more sustainable.\u003c/p\u003e \u003cp\u003eIn 2019, most vehicles in Europe used fuels such as diesel (66.7%) or gasoline (24.55%) and, for these percentages to decrease, two solutions are mentioned (European Environment Agency, 2022): the production of more efficient and less polluting vehicles and the change in the type of fuel used (use of renewable energies).\u003c/p\u003e \u003cp\u003eSignificant increases (in a European context) in the representativeness of electric vehicles are then presented, from 10.70% (2020) to 17.80% (2021) in a period of one year. This result may have been influenced by the implementation of CO2 targets (European Green Deal).\u003c/p\u003e \u003cp\u003eNot far away, Portugal's reality regarding vehicles in circulation by type of fuel (represented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows increases like those of the European Union.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVehicles in circulation by type of fuel, in Portugal\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eFuel Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDiesel\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePetrol\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGPL\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOther\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,182,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,546,659\u003c/p\u003e \u003cp\u003e57.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,587,487\u003c/p\u003e \u003cp\u003e41.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38,982\u003c/p\u003e \u003cp\u003e0.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,923\u003c/p\u003e \u003cp\u003e0.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,083,694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,818,327\u003c/p\u003e \u003cp\u003e62.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,197,118\u003c/p\u003e \u003cp\u003e36.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48,821\u003c/p\u003e \u003cp\u003e0.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,428\u003c/p\u003e \u003cp\u003e0.32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,021,112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,586,548\u003c/p\u003e \u003cp\u003e65.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,252,846\u003c/p\u003e \u003cp\u003e32.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59,401\u003c/p\u003e \u003cp\u003e0.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e122,317\u003c/p\u003e \u003cp\u003e1.74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,090,889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,621,937\u003c/p\u003e \u003cp\u003e65.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,230,882\u003c/p\u003e \u003cp\u003e31.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62,237\u003c/p\u003e \u003cp\u003e0.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e175,833\u003c/p\u003e \u003cp\u003e2.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,249,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,686,198\u003c/p\u003e \u003cp\u003e64.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,250,747\u003c/p\u003e \u003cp\u003e31.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67,677\u003c/p\u003e \u003cp\u003e0.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e244,411\u003c/p\u003e \u003cp\u003e3.37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource: PORDATA, 2024\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen, the trend of the type of fuel is skewed towards combustion vehicles, highlighting the aggregate of diesel vehicles, which, over the years (2010, 2015, 2020, 2021, and 2022) represent more than 1/2 of the total vehicles in circulation. Concerning the set of gasoline vehicles, it presents lower values than diesel vehicles; however, its representativeness is quite high compared to the \"LPG\" and \"Others\" aggregates.\u003c/p\u003e \u003cp\u003eIn 2010, the total number of vehicles on the road was approximately 6,18\u0026nbsp;million, of which 57.37% were diesel-powered and 41.85% were gasoline-powered, reflecting the predominance of traditional internal combustion engines. The weight of LPG vehicles and other categories was residual, representing only 0.63% and 0.14%, respectively.\u003c/p\u003e \u003cp\u003eFive years later, in 2015, there was an increase in the share of diesel vehicles, which rose to 62.76%, while gasoline vehicles fell to 36.11%.\u003c/p\u003e \u003cp\u003eIn 2020, the total number of vehicles reached 7,02\u0026nbsp;million, representing growth of approximately 15.4%\u003csup\u003e4\u003c/sup\u003e compared to 2015. Diesel continues to dominate the market, commanding 65.33% of the total, though its growth rate has exhibited a modest decline in recent years. In contrast, the \"Others\" categories (which principally incorporate electric and hybrid vehicles) will witness a substantial increase, rising from 0.32% in 2015 to 1.74% in 2020.\u003c/p\u003e \u003cp\u003eIn recent years, specifically in 2021 and 2022, there has been a stabilization in the share of diesel vehicles, which remained at approximately 65% in 2021 and 64.65% in 2022. Concurrently, the percentage of gasoline vehicles continued to decline marginally to 31.05%. The most significant growth is observed in the \"other\" fuel sources category, which increased to 3.37% in 2022, more than doubling compared to 2020. This increase signifies a progressive entry of electric and hybrid vehicles into the market, driven by environmental policies, tax incentives, and growing public awareness of climate change.\u003c/p\u003e \u003cp\u003eIn summary, the period from 2010 to 2022 is characterized by a phase of consolidation of diesel as the dominant fuel, followed by the beginning of energy diversification, especially after 2020 (when the percentage reached values above 1%). If this trend continues, it is expected that, in the next decade, alternative energy vehicles will represent an increasingly significant fraction of the national vehicle fleet. This reflects the structural changes underway in the transport sector and in European energy policy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the evolution of values (in percentage) of the various sets, in the period from 2010 to 2022. It is thus possible to observe that all sets, except the aggregate of gasoline vehicles, suffered an increase in this period: diesel vehicles - increased by about 7.30%; gasoline vehicles: decreased by about 11%; LPG vehicles- increased by about 0.30%.; \"Others\" category - increased about 3.20%. Assuming the \"Other\" category as a representative of sustainable vehicles, its positive evolution is notorious.\u003c/p\u003e \u003cp\u003eAlthough the production and disposal of electric vehicles is less eco-friendly compared to traditional vehicles, not only CO2 emissions in their production should be taken into account, but also the use of the vehicle and, subsequently, its disposal/scrapping (European Environment Agency., 2022 and Panthi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaking into account the above, electric vehicles have proven to be more sustainable means of transport than combustion vehicles and, in the future (Bor\u0026eacute;n, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the use of renewable energies (more pronounced) for electricity production will reduce their impact (at least in terms of fuel type) and, through the European Union's plans to produce more sustainable batteries, they will become less harmful to the environment (Directive 2005/36/EC of the European Parliament and of the Council of 7 September 2005, 2015).\u003c/p\u003e"},{"header":"3 Taxi Companies","content":"\u003cp\u003eThe introduction of sustainable transport solutions in urban areas represents a cornerstone of the European Union\u0026rsquo;s broader sustainability agenda. Within this framework, the transition of taxi companies towards environmentally friendly vehicle fleets plays a crucial role in the decarbonization of the mobility sector. However, the high acquisition costs of sustainable vehicles continue to be a barrier for both private citizens and companies, particularly small and medium-sized enterprises (SMEs) operating in the taxi industry (K\u0026ouml;vesdi, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInnovation and the adoption of green technologies within taxi services are recognized as essential steps toward transforming public transport systems (Hofer, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Taxi companies not only provide critical last-mile connectivity but also act as visible representatives of sustainable mobility in cities\u003csup\u003e5\u003c/sup\u003e (Hassouna \u0026amp; Assad, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The economic and environmental impact of these firms thus extends beyond their own operations, influencing broader societal perceptions of sustainable transport. In Portugal, the relevance of taxi companies is reflected in their number and geographical distribution, which make them a key component of the national mobility ecosystem. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the number of taxi operators has fluctuated over recent years, largely influenced by economic cycles, digital platform competition, and regulatory changes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvolution of taxi companies in Portugal\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInfo/Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTaxi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of conductors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26,384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26,984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20,224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of companies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRide-hailing service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRide-hailing operators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12,455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE-platform operators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eSource: Portuguese Institute for Mobility and Transport\u003c/b\u003e\u003csup\u003e6\u003c/sup\u003e \u003cb\u003e(IMT, 2024)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data reveal two important trends: first, the overall stability of traditional taxi firms despite the competitive expansion of ride-hailing services (e.g., Uber, Bolt, and Free Now); and second, the increasing diversification of the sector through digitalization and hybrid operational models. The number of taxi companies has remained relatively constant since 2020, while the rise of app-based mobility platforms has introduced new business models that coexist\u0026mdash;sometimes competitively\u0026mdash;with conventional taxi services.\u003c/p\u003e \u003cp\u003eIn Portugal, Article 53 of the Portuguese Vehicle Tax Code (VTC) establishes a tax benefit designed to support taxi companies in adopting cleaner technologies. Law No. 2/2020 of 31 March grants a 70% exemption on vehicle tax (VT) for passenger and mixed-use vehicles assigned to taxi or rental-with-driver services (vehicles with plates \u0026ldquo;A\u0026rdquo; or \u0026ldquo;T\u0026rdquo;). This exemption applies to vehicles up to four years old, provided their CO₂ emissions remain below 160 g/km (NEDC) or 184 g/km (WLTP). Vehicles equipped exclusively with natural gas, electricity, or hybrid engines are fully exempt from VT (paragraph 2 of Article 53, VTC). To access this benefit, companies must be registered with the Directorate-General of Customs and Excise Duties and submit a specific application form (Form 7 under Article 53, VTC). These fiscal measures aim to accelerate the decarbonization of the taxi fleet by reducing the relative cost of cleaner vehicles. However, the extent to which companies benefit from such incentives depends on their financial stability, size, and regional context, as support Fernandes et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Weber \u0026amp; Rohracher (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Smaller operators often face difficulties meeting administrative requirements or accessing vehicles that comply with emission thresholds, particularly outside metropolitan regions. Therefore, understanding the regional and structural determinants of fiscal benefit uptake among taxi companies is crucial to evaluating the overall effectiveness of sustainable transport policy in Portugal. To answer this, the study compares taxi companies that accessed the VT tax benefit (beneficiary entities) with those that did not. The empirical investigation evaluates whether economic and financial characteristics\u0026mdash;such as profitability, capital structure, and operational costs\u0026mdash;determine access to fiscal incentives for the acquisition of environmentally friendly vehicles. The results of this analysis are presented in the next section.\u003c/p\u003e"},{"header":"4 What is the regional impact of tax incentives on taxi companies?","content":"\u003cp\u003eThe empirical analysis was carried out using data from the SABI\u003csup\u003e7\u003c/sup\u003e database, comprising approximately 14,900 registered taxi companies operating in Portugal. Due to the extensive size of the data set, a representative sample of 630 firms was selected to ensure statistical robustness while maintaining sectoral diversity (315 from Group A and 315 from Group B)\u003csup\u003e8\u003c/sup\u003e. The analysis focuses on identifying whether access to the VT tax benefit significantly influences the financial and operational performance of these companies during the period 2015\u0026ndash;2022. A regional analysis was carried out on the sample (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample\u0026acute;s regional analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of taxi companies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNumber\u003c/b\u003e\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePercentage (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlentejo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlgarve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLisboa e Vale do Tejo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegi\u0026atilde;o Aut\u0026oacute;noma da Madeira\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegi\u0026atilde;o Aut\u0026oacute;noma dos A\u0026ccedil;ores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis by region clearly shows that most Portuguese companies took advantage of the tax benefit in question are from \u003cem\u003eLisboa e Vale do Tejo\u003c/em\u003e (51.4%). It is also evident that within the identified regions, the northern region is near Lisbon. However, the sum of the remaining regions does not reach even 14% of the total beneficiary companies in the period under review. This reveals significant concern regarding the definition of the tax policy in question, regarding regional disparities. However, to provide a comprehensive analysis, it is necessary to consider other factors that are relevant to this study.\u003c/p\u003e \u003cp\u003eFive groups of variables were defined, each representing a specific financial or structural dimension of company performance: (i) profitability indicators (net profit, turnover, operational income, profit margin); (ii) capital efficiency (return on capital invested, solvency ratio); (iii) equity-related measures (equity per employee, equity ratio); (iv) cost-related indicators (average cost per employee); and (v) operational variables (transport and sales services rendered). These variables were examined to test the following hypotheses (H1\u0026ndash;H5):\u003c/p\u003e \u003cp\u003eH1: The averages of net profit, turnover, operational income, sales services rendered, and sales services provided determine access to the VT tax benefit.\u003c/p\u003e \u003cp\u003eH2: The averages of return on capital invested and profit margin, as profitability variables, influence the likelihood of accessing the VT tax benefit.\u003c/p\u003e \u003cp\u003eH3: The solvency ratio average, as a structural variable, affects access to the VT tax benefit.\u003c/p\u003e \u003cp\u003eH4: The averages of equity per employee and average cost per employee determine access to the VT tax benefit.\u003c/p\u003e \u003cp\u003eH5: The transport average determines access to the VT tax benefit.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGroup analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAn Independent-Samples Mann\u0026ndash;Whitney U Test was conducted to compare the distribution of each variable across two categories of Total Tax Benefit (TBF): beneficiary and non-beneficiary companies. The test was chosen because it does not assume normal distribution of data and is suitable for comparing independent samples of unequal sizes. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the hypothesis testing outcomes. Variables with p-values below 0.05 indicate statistically significant differences between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypothesis Test Summary (Mann-Whitney U Test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull Hypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig.\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of return _on _capital _invested average is the same across categories of TBF.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;,001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReject the null hypothesis.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of solvency_ratio_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetain the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of equity_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetain the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of avacost_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReject the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of salesRE_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetain the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of salesPROV_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetain the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of transp_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReject the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of net_profit_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReject the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of turnover_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetain the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of operacional_inc_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetain the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe distribution of profit_margin_average is the same across categories of TBF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent-Samples Mann-Whitney U Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetain the null hypothesis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ea. The significance level is ,050.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eb. Asymptotic significance is displayed.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe test results, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, reveal statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for four variables: return on capital invested, average cost per employee, transport average, and net profit average. Specifically, companies that benefited from tax incentives demonstrated higher capital efficiency, profitability, and transport capacity, but lower average employee costs. These results suggest that beneficiary companies tend to manage resources more efficiently and operate at greater scale compared to non-beneficiaries. Conversely, the solvency ratio, equity average, sales services rendered, sales services provided, turnover, operational income, and profit margin showed no significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that these variables do not independently influence access to fiscal incentives. This suggests that while profitability and operational activity are linked to benefit uptake, structural stability and sales volume alone are not sufficient determinants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA Spearman correlation analysis was performed to identify relationships between total tax benefits (2015\u0026ndash;2022) and the financial indicators for Group A (beneficiary companies).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlation coefficients\u003csup\u003e10\u003c/sup\u003e (Group A)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003etotal_tax_benefits_2015_2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal_tax_benefits_2015_2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereturn _on _capital _invested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.223**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esolvency_ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eequity_per_employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaverage_cost_per_employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esales_services_rendered_equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esales_services_provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.262**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enet_profit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.288**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eturnover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.243**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoperacional_income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprofit_margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Spearman correlations are significant at **the 0.01 level (2-tailed) and at *the 0.05 level (2-tailed)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results indicate several positive and statistically significant correlations: net profit (ρ\u0026thinsp;=\u0026thinsp;0.288*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), sales services provided (ρ\u0026thinsp;=\u0026thinsp;0.262**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), operational income (ρ\u0026thinsp;=\u0026thinsp;0.246**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), turnover (ρ\u0026thinsp;=\u0026thinsp;0.243**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), return on capital invested (ρ\u0026thinsp;=\u0026thinsp;0.223**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and transport (ρ\u0026thinsp;=\u0026thinsp;0.140*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings demonstrate that companies with stronger financial performance and higher levels of operational activity are more likely to benefit from tax incentives. This relationship reflects the dual purpose of fiscal policy in promoting both sustainability and competitiveness within the sector. By contrast, the solvency ratio (ρ = \u0026minus;0.015), equity per employee (ρ\u0026thinsp;=\u0026thinsp;0.072), average cost per employee (ρ = \u0026minus;0.071), sales services rendered (ρ\u0026thinsp;=\u0026thinsp;0.035), and profit margin (ρ = \u0026minus;0.052) exhibited weak or non-significant correlations. Thus, factors such as capital structure and labor cost intensity appear less relevant in determining tax benefit outcomes. Overall, correlation analysis supports the hypothesis that profitability and operational efficiency are the main predictors of access to fiscal benefits, while cost structure and solvency play only a marginal role.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCausal model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eComparing taxi companies that benefited from tax incentives for less polluting cars (Group A: a database of 237 taxi companies) with others in the same sector that did not (Group B: database of 6,773 taxi companies, including Group A companies), the ANOVA results show that there are differences between companies that did and did not take advantage of the tax benefits under investigation, so the authors applied the regression model to Group A only.\u003c/p\u003e \u003cp\u003eMultiple linear regression was used to verify whether access to environmental tax benefits for less polluting cars can be predicted by taxi companies\u0026acute; economic and financial features, i.e., the authors applied a model to explain which characteristics of taxi companies are significant in inducing access to environmental tax incentives under study. The dependent variable is \u003cem\u003eTaxb\u003c/em\u003e [average tax benefits for less polluting cars]. The independent variables are: \u003cem\u003eOpres (operational result), NetProf (net profit)\u003c/em\u003e, \u003cem\u003eTasset (total assets), Conprf, Emplnb\u003c/em\u003e, \u003cem\u003eTurn (turnover)\u003c/em\u003e, \u003cem\u003ePerf (performance)\u003c/em\u003e, \u003cem\u003eGVA (Gross Value Added), Solv (solvency ratio), Transeq (transport equipment), IncTax (income tax), eqRat, Fage, ProfMG (profit margin), Debt\u003c/em\u003e. U\u003csub\u003ei\u003c/sub\u003e is the error term, relative to unknown factors. The mathematical specification of the regression model is:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eTaxb\u003c/em\u003e \u003csub\u003e \u003cem\u003ei\u003c/em\u003e \u003c/sub\u003e = B\u003csub\u003e0\u003c/sub\u003e + B\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eOpres\u003c/em\u003e1\u003csub\u003ei\u003c/sub\u003e+ B\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eNetProf\u003c/em\u003e2\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;B\u003csub\u003e3\u003c/sub\u003e\u003cem\u003eTasset3\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e + B\u003csub\u003e4\u003c/sub\u003ee\u003cem\u003eConprf\u003c/em\u003e4\u003csub\u003ei\u003c/sub\u003e+ B\u003csub\u003e5\u003c/sub\u003e\u003cem\u003eEmplnb\u003c/em\u003e5\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;B\u003csub\u003e6\u003c/sub\u003e\u003cem\u003eTurn\u003c/em\u003e6\u003csub\u003ei\u003c/sub\u003e + B7\u003cem\u003ePerf\u003c/em\u003e7\u003csub\u003ei\u003c/sub\u003e+ B\u003csub\u003e8\u003c/sub\u003e\u003cem\u003eGVA\u003c/em\u003e8\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;B\u003csub\u003e9\u003c/sub\u003e\u003cem\u003eSolv\u003c/em\u003e9\u003csub\u003ei+\u003c/sub\u003e B\u003csub\u003e10\u003c/sub\u003e\u003cem\u003eTranseq\u003c/em\u003e10\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;B\u003csub\u003e11\u003c/sub\u003e\u003cem\u003eIncTax\u003c/em\u003e11\u003csub\u003ei\u003c/sub\u003e + B\u003csub\u003e12\u003c/sub\u003e\u003cem\u003eeqRat1\u003c/em\u003e2\u003csub\u003ei\u003c/sub\u003e + B13\u003cem\u003eFage\u003c/em\u003e13\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;B\u003csub\u003e14\u003c/sub\u003e\u003cem\u003eProfMG\u003c/em\u003e14\u003csub\u003ei\u003c/sub\u003e + B\u003csub\u003e15\u003c/sub\u003e\u003cem\u003eDebt\u003c/em\u003e15\u003csub\u003ei\u003c/sub\u003e+ U\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eThe dependent variable is the total amount of tax benefits obtained between 2015 and 2022, while the independent variables are sales services provided, profit margin, equity per employee, average cost per employee, and net profit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCausal Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eModel Summary\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdjusted R Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eChange Statistics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR Square Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSig. F Change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.706\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2223.56250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003ea. Predictors: (Constant), salesPROV_mean, profit_margin_mean, equity_mean, avacost_mean, net_profit_mean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eb. Dependent Variable: total_tax_benefits_2015_2022\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe model produced a strong fit (R\u0026thinsp;=\u0026thinsp;0.706; R\u0026sup2; = 0.498; Adjusted R\u0026sup2; = 0.490), indicating that approximately 49.8% of the variance in total tax benefits can be explained by the selected predictors. The model\u0026rsquo;s F-statistic (F\u0026thinsp;=\u0026thinsp;61.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) confirms that the regression equation is statistically significant, meaning that the independent variables collectively provide a robust explanation for differences in tax benefit allocation among taxi companies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnstandardized Coefficients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSig.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(Constant)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3978,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e312,296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etransp_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enet_profit_mean***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-114,683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprofit_margin_mean**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eretcapinv_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esolvrat_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eequity_mean**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eavacost_mean***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-231,962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6,516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esalesRE_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esalesPROV_mean***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eAdjusted R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e 49.9%\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis of coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) reveals the following significant effects: Net profit average (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;114.683, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): negative and significant, suggesting that higher profits are associated with reduced fiscal benefits\u0026mdash;possibly reflecting eligibility limits or diminishing marginal benefits for more profitable firms. Average cost per employee (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;231.962, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): negative and significant, indicating that companies with higher labor costs tend to obtain lower tax advantages. Profit margin (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.002, p\u0026thinsp;=\u0026thinsp;0.029): small but significant negative effect, showing that higher profitability margins slightly decrease benefit access. Equity per employee (B\u0026thinsp;=\u0026thinsp;15.229, p\u0026thinsp;=\u0026thinsp;0.017): positive and significant, meaning firms with greater capitalization per employee are more likely to secure higher benefits. Sales services provided (B\u0026thinsp;=\u0026thinsp;32.769, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): positive and highly significant, confirming that greater business activity and service output increase the probability and scale of fiscal benefits received. The regression results reveal a dual fiscal\u0026ndash;economic dynamic: while tax benefits encourage investment in sustainable transport, they are more accessible to financially healthier firms, potentially exacerbating regional and structural disparities. Firms with greater equity and stronger service performance receive proportionally higher tax benefits, whereas smaller firms with higher costs and limited profits benefit less. This pattern underlines the necessity of designing fiscal mechanisms that not only incentivize sustainability but also ensure equitable access across different company profiles. In essence, the analysis confirms that fiscal policy can effectively shape sustainable transformation when it is complemented by measures that account for firm level and regional heterogeneity.\u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThe results of the statistical analysis suggest a dual relationship between financial performance and fiscal incentives. On one hand, the provision of tax benefits clearly promotes sustainable investment within the sector. On the other hand, the accessibility of these benefits appears to be conditioned by firms\u0026rsquo; pre-existing financial strength. This creates a paradox where economically robust firms benefit more, potentially widening regional and structural disparities. The data also reveal that operational efficiency\u0026mdash;captured by sales service activity and equity intensity\u0026mdash;is a strong determinant of fiscal benefit access. Smaller companies with limited resources face higher barriers, such as compliance costs or credit constraints, which may prevent them from adopting sustainable technologies despite the existence of tax incentives. These results highlight the importance of aligning fiscal policy design with regional and structural realities. Policymakers should consider the introduction of complementary measures\u0026mdash;such as targeted subsidies, simplified procedures for small firms, or technical assistance programs\u0026mdash;to ensure that fiscal incentives contribute not only to environmental goals but also to social and territorial cohesion. The findings confirm the critical role of fiscal instruments in facilitating sustainable transitions, particularly in high-emission sectors such as transport. In Portugal, targeted tax incentives for taxi companies shows how fiscal mechanisms can integrate environmental and economic goals simultaneously. Nonetheless, the persistence of regional disparities in the uptake of benefits reveals that fiscal frameworks must become more territorially adaptive and context sensitive. From a policy design perspective, this implies that national governments should incorporate regional specificities into fiscal planning, considering market maturity, fleet characteristics, and infrastructure availability. Uniform policies may unintentionally reproduce inequalities between metropolitan and peripheral areas. Therefore, adaptive fiscal instruments\u0026mdash;coordinated with local authorities\u0026mdash;are essential to promote a fair and balanced transition. These results align with contemporary research on sustainable transitions, which argues for multi-level governance and adaptive policymaking. Fiscal policies, while effective in encouraging the adoption of cleaner technologies, must be supported by complementary tools such as low-interest financing, simplified administrative procedures, and investments in charging networks. Integrating these mechanisms would ensure that fiscal incentives deliver both environmental and socioeconomic benefits. Overall, the study underscores the potential of fiscal policies to function not only as revenue instruments but also as levers for systemic change. To achieve this, public decision-makers should combine tax design with spatial analysis, monitoring territorial outcomes to ensure that policies contribute to cohesive and inclusive sustainability transitions.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThis study shows that fiscal incentives are powerful instruments for steering the transport sector toward sustainability. Profitability and cost-efficiency indicators show significant positive correlations with tax benefits, indicating that economically resilient companies are better positioned to engage in sustainable transitions. In contrast, higher average costs per employee and profit margins are associated with reduced access to benefits, reflecting the complexity of tax\u0026ndash;performance dynamics.\u003c/p\u003e \u003cp\u003eThe results confirm that well-designed fiscal instruments can effectively balance environmental objectives with business competitiveness. However, to maximize their impact, fiscal policies must integrate territorial differentiation. Local socioeconomic structures, access to electric infrastructure, and company size all shape the ability of firms to benefit from tax incentives.\u003c/p\u003e \u003cp\u003eAccordingly, policymakers should adopt a multi-level, data-driven approach to fiscal governance. Integrating spatial and performance data into fiscal planning would allow more equitable access to tax benefits, strengthening both environmental and social cohesion. The Portuguese case provides an example of how fiscal tools can foster transformative change when aligned with broader sustainable development strategies.\u003c/p\u003e \u003cp\u003eIn line with the European Green Deal, these findings highlight that achieving climate neutrality by 2050 requires fiscal systems that reward sustainable investment and penalize environmentally harmful practices. By embedding territorial analysis into fiscal design, public decision-makers can ensure that the green transition becomes not only sustainable but also just.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.F and A.D wrote the main manuscript text and prepared all the figures . All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was supported by national funds through the FCT \u0026ndash; Portuguese Foundation for Science and Technology, I.P., by the project reference 2023.12454.PEX and DOI identifier: https://doi.org/10.54499/2023.12454.PEX.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGiacomelli M, G\u0026ouml;rg\u0026uuml;n EK, Salata S, Ronchi S, Bernardini C, Costa MM, Arcidiacono A, Concilio G (2025) Climate neutrality and urban planning: A state of the art from literature and the European cities. In \u003cem\u003eSustainable Cities and Society\u003c/em\u003e (Vol. 130). 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Springer, Cham. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-37312-2_9\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-37312-2_9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes F, Dinis A, Pereira L, Carvalho A (2024) Sustainable tax policy for taxi companies: a proposal for an evaluation model of Vehicle Tax Incentive. Revista Jur\u0026iacute;dica Portucalense 426\u0026ndash;449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.34625/issn.2183-2705(36)2024.ic-19\u003c/span\u003e\u003cspan address=\"10.34625/issn.2183-2705(36)2024.ic-19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e t CO2eq\u0026thinsp;=\u0026thinsp;ton of carbon dioxide equivalent\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e European Green Deal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal_en\u003c/span\u003e\u003cspan address=\"https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal_en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The \"Other\" category aggregates the subcategories \"Biodiesel, Pure electric, Plug-in hybrid electric, non-plug-in hybrid electric, and Other\".\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Rate of change = [(Vf - Vi)/Vi]*100\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Considering taxi companies as an integral part of the public transport sector.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.imt-ip.pt/\u003c/span\u003e\u003cspan address=\"https://www.imt-ip.pt/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e SABI database: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://login.bvdinfo.com/R1/SabiNeo?SetLanguage=pt\u003c/span\u003e\u003cspan address=\"https://login.bvdinfo.com/R1/SabiNeo?SetLanguage=pt\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Group A refers to taxi companies that have taken advantage of the tax benefit outlined in Article 53 of the VTC. Consequently, Group B refers to companies that have not accessed this tax benefit. The information was obtained from the SABI database and the Portuguese Finance Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.portaldasfinancas.gov.pt/pt/home.action\u003c/span\u003e\u003cspan address=\"https://www.portaldasfinancas.gov.pt/pt/home.action\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e It is important to note that the number of companies per region shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is the total number of companies in Groups A and B combined (50/50).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The Spearman correlation measures the strength and direction of the monotonic relationship between two variables, with values ranging from \u0026minus;\u0026thinsp;1 to 1. Positive values indicate a direct relationship, meaning that as one variable increases, the other tends to increase as well, while negative values indicate an inverse relationship, where an increase in one variable is associated with a decrease in the other. The p-values indicate the significance of the correlation. Significant correlations at α\u0026thinsp;=\u0026thinsp;0.01 are marked as **, while significant correlations at α\u0026thinsp;=\u0026thinsp;0.05 are marked as *.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sustainable transition, Fiscal policy, Tax incentives, Transport sector, Taxi companies","lastPublishedDoi":"10.21203/rs.3.rs-8109313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8109313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the role of fiscal policy in facilitating a sustainable transition within the Portuguese transport sector, with a particular focus on tax incentives for taxi companies. Using quantitative data from SABI and official statistics for the period 2015\u0026ndash;2022, the research analyzes the relationship between tax benefits and financial and operational performance. Results from Mann\u0026ndash;Whitney U and regression tests show that profitability indicators, namely net profit, operational income, and sales of services provided, significantly influence the level of tax benefits received. The findings suggest that fiscal policy plays a crucial role in fostering sustainable mobility while enhancing business performance. However, regional disparities highlight the need for more territorially adaptive fiscal frameworks. This paper, therefore, contributes to our understanding of how fiscal measures can be strategically aligned with sustainability goals to ensure equitable and effective transitions across regions.\u003c/p\u003e","manuscriptTitle":"Assessing sustainable transition through fiscal policy: a regional analysis of tax incentives for taxi companies in Portugal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 08:27:13","doi":"10.21203/rs.3.rs-8109313/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-03T14:04:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T09:02:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-18T07:28:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Annals of Regional Science","date":"2025-11-13T23:35:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9dfce306-3382-42e8-9095-f5a95d42da4d","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T08:27:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 08:27:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8109313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8109313","identity":"rs-8109313","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}