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Environmental Assessment and Sustainable Development in the United States

New Mexico Institute of Mining & Technology, Department of Management, 801 Leroy Place, Socorro, NM 87801, USA
Tokyo Institute of Technology, Tokyo Tech World Research Hub Initiative, School of Environment and Society, 3-3-6 Shibaura, Minato-ku, Tokyo 108-0023, Japan
Northeastern University, College of Professional Studies, 360 Huntington Ave, Boston, MA 02115, USA
Author to whom correspondence should be addressed.
Academic Editor: Brian D. Fath
Energies 2021, 14(4), 1180;
Received: 15 January 2021 / Revised: 7 February 2021 / Accepted: 16 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue New Challenges in Energy and Environment)


This study aims to overview the U.S. sustainable development by measuring the environmental performance of 50 states over the period of 2009–2018. To attain the objective, we employ data envelopment analysis for environmental assessment where we prioritize the minimization of CO2 emissions first and the maximization of gross state product later under the concept of managerial disposability (i.e., an environment-based performance measure). Then, we examine how the state-level environmental performance measures are associated with their political and spatial contexts. For the purpose, we conduct the Kruskal-Wallis rank sum test across groups of states characterized by their political transitions in the presidential and gubernatorial elections and defined by the regions of the U.S. Economic Development Administration and Environmental Protection Agency. Based on our empirical results, we find that (a) overall environmental performance has gradually enhanced over time, (b) there are statistically significant differences in the environmental performance measures along with the political transitions, and (c) states on both coasts have outperformed those of the middle in the measurement.
Keywords: data envelopment analysis; environmental assessment; sustainable development data envelopment analysis; environmental assessment; sustainable development

1. Introduction

The United Nations Conference on Environment and Development (or the Rio de Janeiro Earth Summit) in 1992 and ensuing pacts, such as the Kyoto Protocol in 1997 and the Paris Agreement in 2016, have impacted the public’s awareness of and attitude toward sustainable development around the world. It was true for the United States but it has had different impacts on different groups depending on the contexts in which they were situated. A spatial context is one of them. More environmentally friendly states, such as California and Massachusetts, have aggressively formulated and implemented environmental policies (particularly, climate policy programs that seek to mitigate and adapt themselves to climate change and its adverse consequences) while their counterparts, such as Montana and North Dakota, have played a passive role (for instance, they are still relying on the production or use of fossil fuels or are reluctant to address environmental or climate issues). A political context also matters. There have been historically many debates over environmental or climate issues (e.g., the establishment of the U.S. Environmental Protection Agency in 1970 and the more recent withdrawal from the Paris Accord) drawing on partisan identification and political ideology. Democrats or liberals tend to place more value on the environment (e.g., environmental protection or spending) than Republicans or conservatives do [1].
Such divergence in environmental awareness and attitude, however, did not date back to a long time ago. As Baldassarri and Gelman [2] argued, the degree of issue partisanship over environmental concern was low and environmental protection or spending was not a contentious issue among different voters prior to the late 1970s. Since then (particularly, after the Rio Summit in 1992), however, it has not been the case anymore [3]. As environmental issues emerge as a politically important agenda, public opinion and politicians’ preferences over the environment have diverged across geographic regions and political beliefs [4]. For instance, Democrats tend to be more aware of environmental issues and more supportive of environmental spending than Republicans do [5]. From the establishment of the U.S. Environmental Protection Agency (EPA) to a series of environment/climate-related pacts, environmental awareness and attitude became one of the litmus tests that identify people’s geographic location and political partisanship.
Polarization on environmental/climate concern culminates in the Trump Administration’s withdrawal from the Paris Agreement and the to-be-soon Biden Administration’s resolve to return to the Agreement. Since each administration relies on voters who have different preferences over the environment/climate, their decisions show stark contrast against each other. The U.S. withdrawal from the Agreement threatened its (or federal) leadership on the environmental issues on one hand, but it offered sub-national entities (e.g., states or cities) an opportunity to exert their environmental leadership [6]. While the EPA (a federal environmental agency) is staffed by climate deniers rather than environmental activists, some states (alone or consortium) have taken pivotal environmental measures. States on both coasts, for instance, have made decarbonization efforts such as the “West Coast Electric Highway” initiative and the establishment of the “Northeast States for Coordinated Air Use Management.” In the electricity sector, particularly, some states have transitioned from fossil fuels to renewables [7,8]. In the transportation sector, additionally, they have deployed low- or zero-emission vehicles [9].
In this vein, this study aims to measure the environmental performance of 50 states in the United States (U.S.) over the past decade (from 2009 to 2018) and explore how political and spatial contexts influence states’ environmental performance. To that end, we employ data envelopment analysis for environmental assessment (DEA-EA) to evaluate state-level environmental performance and then conduct a series of the Kruskal-Wallis tests to examine whether there are statistical differences in the environmental performance across states with different partisan identification and political ideology and with different regional environment.
The remaining sections are organized as follows: Section 2 conducts a literature survey on the DEA applications to the state-level performance evaluation. Section 3 and Section 4 describe the underlying concepts of DEA-EA and elucidate our proposed DEA-EA as an approach to evaluate the performance of U.S. states. Section 5 summarizes our empirical results obtained from the analysis. Section 6 concludes this study along with future extensions.
All abbreviations used in this study are summarized as follows: BTU: British thermal unit, D: Democratic, DEA: Data Envelopment Analysis, DMU: Decision Making Unit, DTS: Damages to Scale, EA: Environmental Assessment, EDA: Economic Development Administration, EPA: Environmental Protection Agency, GSP: Gross State Product, NESCAUM: Northeast States for Coordinated Air Use Management, MMT: Million Metric Tons, R: Republican, R&D: Research and Development, URS: Unrestricted and U.S.: United States.

2. Previous Studies

2.1. State-Level Performance Measurement

Table 1 lists previous studies of measuring various types of performance assessment in the U.S. The. institutions are based on federalism where the federal and state governments split power. Except for interstate concerns (e.g., national security), state governments have the authority to collect taxes, and formulate and implement policy programs that reflect on the needs and desires of their own constituents. Since state governments’ policy programs affect all entities in their jurisdictions, states can be regarded as a decision making unit (DMU) in this study and the assessment of their performance can attract the public’s attention. It is particularly true from the perspective of constituents who want to maximize their utilities in various ways (e.g., voting for their economic interests and/or political preferences).
Despite its eligibility for the DMU, surprisingly, there is a paucity of studies on assessing the state-level performance. A majority of studies use macroscopic (e.g., nations) or microscopic (e.g., companies, hospitals, and schools) entities as DMUs. As a mesoscopic entity, states have some advantages over or differences from other government units (e.g., counties or cities) when used as a DMU. Some of them are as follows: First, states have some degree of latitude to decide how to expend their budgets, which leads to heterogeneous policy sets and make some states stand out from others. While each state’s budget items are almost homogeneous (e.g., education, public health, corrections, etc.), the mix of budget items are somewhat different across states. Second, the various policy sets stem from political elites or leaders that need to listen to their constituents’ voices. Presidential or gubernatorial candidates should reflect their policy agenda on the interests of voters to win the elections. Third, states are nested in their regions (agglomerates of multiple states) that often characterize states’ sociocultural, industrial, economic, environmental, and political contexts. Thus, states in the same regions tend to share similar identities and sometimes facilitate them to cooperate or form an alliance to attain the same goals. Lastly, state-level data tend to be more accessible than county or city-level data. Public or private sources in the U.S. offer at least state-level data so that data availability issues can be addressed.
As summarized in Table 1, many previous studies are mainly concerned with the state level. For instance, Lee and Joo [11], Thomas et al. [13], and Gearhart [15] assessed states’ performance in the fields of corrections, research and development, and health care, respectively. Of the studies in Table 1, Park et al. [17] and Halkos and Polemis [18] evaluated environmental performance in the transportation and electricity sectors, respectively. While both studies focused on the environmental performance of 50 states, they were sector-specific and their data were relatively outdated (up to 2012) so that the studies could not capture more recent focusing events, such as the political transition from Obama Administration to Trump Administration, which lead to significant policy changes.

2.2. Political and Spatial Contexts on Climate/Environmental Policy

A political context can influence the formulation and implementation of climate/environmental policy. A clear example is partisan sorting where elite cues impact mass opinion on climate/environmental issues and the public opinion becomes more divergent so that Democrats move left (i.e., to the pro-environment) and Republicans do right (i.e., to the anti-environment) [19]. Such polarization of both politicians and the public has been substantial, particularly since the 1990s [3]. While there were pivotal global events such as the Rio Earth Summit and the Kyoto Protocol at that time, the Republican took over Congress from the Democratic in the U.S. so that policy hegemony was shifted to the conservatives [1]. The disharmony between the external pro-environmental movement and internal anti-environmental movement rendered Democrats and Republicans move in the opposite direction, which created the polarization over environmental protection and spending.
Based on the small-government doctrine oriented toward laissez-faire or marketized and privatized economy, which restricts government interventions such as environmental regulations, the Republican elites have placed more value on economic development rather than environmental protection. Such positions have been maintained particularly under the Republican presidents and in the Republican-ruling states. With the inauguration of the Obama Administration in 2009, however, the Democratic leaders recognized science-based climate risks and embraced climate actions such as mitigation and adaptation measures. For instance, clean energy innovation and transportation decarbonization became an important political agenda and they were placed in the front burner, which previously was in the back burner. While the Trump Administration has filled environment-related positions with climate deniers, it would be dramatically changed with the start of the Biden Administration in 2021.
To take such political transition into account, we explore each state’s partisans that have won presidential and gubernatorial elections over the past decade and categorize states into four groups: D to D (the Democratic to the Democratic), R to D (the Republican to the Democratic), D to R (the Democratic to the Republican), and R to R (the Republican to the Republican). Table 2 summarizes the election results and political transition in 50 states. Considering the role of the political context in the climate/environmental policy, we construct the first hypothesis as follows:
Hypothesis 1.
Political context influences the states’ environmental performance.
Hypothesis 1a (H1a).
States’ environmental performance varies significantly by political transition by presidential elections.
Hypothesis 1b (H1b).
States’ environmental performance varies significantly by political transition by gubernatorial elections.
A spatial context also matters in the climate/environmental policy. In the U.S. that boasts its vast territory, particularly, geographic conditions vary by region and residents are impacted by their different regional situations. With the emerging role of state governments in managing climate risks, they have played a key role in the formulation and implementation of climate policy [20]. In the clean energy policy area, for instance, state governments have created many policy innovations [21]. However, climate actions were not limited to each state’s independent measures. States have interacted with their neighboring states/regions [22] and state initiatives have gradually evolved into regional collaborations [23]. The Northeast States for Coordinated Air Use Management (NESCAUM) and the Regional Electric Vehicle Plan for the West are examples of state/regional efforts to deploy electric vehicles as a means to address climate issues.
In this regard, there is a great body of studies that focused on regional variations in climate/environmental policy. For instance, regional assessments have been conducted in the fields of watershed management [24], environmental inequality (particularly, industrial air toxics exposure) [25], perceptions about climate change [26], and public opinion on climate change [27]. However, few studies shed light on regional variations in environmental performance. To address this issue, we use the regional schemes proposed by EDA and EPA in that two federal agencies deal with two important outputs (economic development and environmental protection) in evaluating state-level performance. Table 3 shows the EDA and EPA regions and their member states. Drawing on the regional schemes, we construct the second hypothesis as follows:
Hypotheses 2.
Spatial context influences states’ environmental performance.
Hypotheses 2a (H2a).
States’ environmental performance varies significantly by EDA regions.
Hypotheses 2b (H2b).
States’ environmental performance varies significantly by EPA regions.

3. Underlying Concepts

We apply DEA-EA to the prepared data set, which contains not only a column vector (X) of m inputs and that of G of s desirable outputs but also a column vector (B) of h undesirable outputs. Please note that a conventional use of DEA excludes the existence of B in the computational process although the environmental assessment needs a unification process between G and B. The unification process is classified under two (natural and managerial) disposability concepts. We focus upon the concept of “managerial disposability” because we are interested in environmental assessment. See the study [28] that provides a use of DEA for natural disposability. This research is an extension of the work by shifting natural to managerial disposability.
Natural Disposability: This research starts the concept of sustainability from a description of “natural disposability” in which the first priority is economic prosperity and the second one is pollution prevention. This type of disposability implies the elimination of inefficiency within the framework of performance assessment. In the concept, an inefficient DMU decreases some components of X or maintains them at their current level. The X decrease occurs with increasing some components of G. The decrease of X naturally reduces B. The previous DEA studies did not consider an existence of B.
Managerial disposability: The concept discussed in this study is the opposite of natural disposability. For example, a coal-fired power plant increases the amount of coal combustion to increase the amount of electricity generation. Here, even if the power plant increases the amount of coal combustion, the increase can reduce the amount of CO2 emission by a managerial effort such as a use of high-quality coal with less CO2 emission and/or an engineering effort to use new generation technology (e.g., clean coal technology) that can reduce the amount of CO2 emission. Management of the power company considers such a change as a business opportunity to adjust them to a change of environmental regulation. Under the managerial disposability, the investment in green technology may provide firms with an opportunity to enhance not only environmental protection but also economic success. Thus, both economic prosperity and green technology are not mutually exclusive in modern business. Rather, we need to consider that both are necessary conditions toward sustainable development. This type of disposability was never considered in the previous DEA studies.
Null-Joint Hypothesis: An important concept to be thought of is the null-joint relationship between G and B. The hypothesis implies that components of B are “by-products” of G. In other words, B cannot exist without G. The concept is straight forward in discussing the relationship between G and B if we do not consider technology advancement and governmental regulation on B. Thus, it is necessary for us to consider the assumption between G and B when examining a unified efficiency measure under managerial disposability.

4. Method

This subsection describes mathematical formulations to measure the degree of unified efficiency (operational and environmental) using a forecasted data set. The nomenclatures are specified in the following manner:
  • X : A column vector of m inputs,
  • x i j t : The i th input of the j th DMU at the t th period,
  • G : A column vector of s desirable outputs,
  • g r j t : The r th desirable output of the j th DMU at the t th period,
    B: A column vector of h undesirable outputs,
  • b f j t : The f th undesirable output of the j th DMU at the t th period,
  • ξ k t : An inefficiency score of the k th DMU at the t th period,
  • d i x : A slack variable of the i th input,
  • d r g : A slack variable of the r th desirable output,
  • d f b : A slack variable of the f th undesirable output,
  • λ j t : A vector of intensity variables on the j th DMU at the z th period,
  • ε s : A prescribed very small number,
  • R i x : A data range related to the i th input
  • R f b : A data range related to the f th undesirable output,
  • t: The observed t th period (t = 1,.., T).
This study specifies the following two types of data ranges (R) according to the upper and lower bounds of production factors:
R i x = ( m + s + h ) 1 ( m a x j { x i j t | j = 1 , , n   &   t   =   1 ,   . . ,   T   } m i n j { x i j t | j = 1 , , n   &   t   =   1 ,   . . ,   T } ) 1 & R f b = ( m + s + h ) 1 ( m a x j { b f j t | j = 1 , , n   &   t   =   1 ,   . . ,   T   } m i n j { b f j t | j = 1 , , n   &   z   =   1 ,   . . ,   T } ) 1 .
The purpose of these ranges is that DEA results can avoid an occurrence of zero in multipliers. Such an occurrence implies that corresponding production factors (X, G and B) are not fully used in the evaluation.
Unified Efficiency: This research assumes that there are n DMUs at the t th period to be examined and all of their production factors are strictly positive even if they are imprecise. All DMUs are specified by j = 1, …, n in the proposed formulations. This study uses the following formulation to compute the unified efficiency of the specific k th DMU under managerial disposability at the specific t th period:
Maximize   ξ k t + ε s ( i = 1 m R i x d i k t x + + f = 1 h R f b d f k t b )   s . t . t = 1 T j = 1 n x i j t λ j t d i x + = x i k t   ( i   =   1 ,   . .   ,   m   &   specific   t   ) ,   t = 1 T j = 1 n g r j t λ jt ξ k t g rkt = g rkt   ( r   =   1   ,   . .   ,   s   &   specific   t   ) ,   t = 1 T j = 1 n b f j t λ jt   + d f b + ξ k t b fkt = b fkt   ( f   =   1   ,   . .   ,   h   &   specific   t   ) ,   λ jt   0   ( j = 1 ,   . .   ,   n   &   t   =   1 ,   . . ,   T   ) ,   ξ k t : URS   ( k :   specific   &   t :   specific   ) ,     d ikt   x + 0   ( i = 1 , . . , m )   &   d f k t b 0   ( f = 1 , . . , h ) .
Model (1) has eight unique features to be noted. First, the period (t = 1, …, T) is used for observed periods. All the periods (t) are together and used in the form of a cross-sectional structure. Second, the unknown vector λ j t = ( λ 1 t , , λ n t ) T r is referred to as “structural” or “intensity” variables in the DEA terminology. They connect all the production factors (X, G and B). Third, the production and pollution possibility set of Model (1) assumes constant Damages to Scale (DTS) because j = 1 n λ jt =   1 does not exist from (1). See [29] for a detailed description on DTS. Fourth, Model (1) considers only single-sided input deviations ( d i x + = t = 1 T j = 1 n x i j t λ j t x i k t 0 ) on all input factors to attain the status of managerial disposability. Fifth, a scalar value ( ξ k t ) stands for a unified inefficiency score that measures a distance between two efficiency frontiers and an observed vector of G and B of the k th DMU at the t th period. Sixth, a small scalar value (e.g., ε s = 0.001) indicates the relative importance between the inefficiency measure and the total sum of slacks. The value ( ε s ) is not a non-Archimedean small number that has been used for mathematical convenience in standard DEA. The small number should be prescribed by a use(s) in the range that the efficiency measure of all DMUs locates between zero (standing for full efficiency) and unity (standing for full inefficiency). Seventh, this type of measurement belongs to the “Debreu-Farrell” criterion. The reference [29] provides a detailed description on the criterion. Finally, the equations, t = 1 T j = 1 n g r j t λ jt ξ k t g rkt = g rkt on desirable outputs, drop slacks related to G to incorporate a possible occurrence of green technology.
A unified efficiency measure (UEM) of the k th DMU at the t th period is measured by
U E M k t = 1 [ ξ k t +   ε s ( i = 1 m R i x d i k t x + + f = 1 h R f b d f k t b ) ] .
here, the inefficiency measure and all slack variables are determined on the optimality of Model (1). The degree of unified efficiency is obtained by subtracting the level of inefficiency from unity as specified in Equation (2).
An important feature of Model (1) is that it specifies the upper bound of inputs by increasing X and reducing B as specified by t = 1 T j = 1 n x i j t λ j t =   x i k t + d i k t x + (i = 1, …, m) and t = 1 T j = 1 n b f j t λ j t = b fkt     d f b   ξ k t b fkt (f = 1, …, h) on optimality. The model also considers that the components of G do not have any slack in the formulation.
Unified Index: To extend the efficiency measure to its corresponding index measure, we modify Model (1) as follows:
Maximize   ξ k t + ε s ( i = 1 m R i x d i k t x + + f = 1 h R f b d f k t b )   s . t .   j = 1 n x i j t 1 λ j t 1 d i k t x + = x i k t   ( i   =   1 ,   . .   ,   m   &   specific   t   =   2 ,   . . ,   T ) ,   j = 1 n g r j t 1 λ jt 1 ξ k t g rkt = g rkt   ( r   =   1   ,   . .   ,   s   &   specific   t   =   2 ,   . . ,   T ) ,   j = 1 n b f j t 1 λ jt 1   + d f k t b + ξ k t b fkt = b fkt   ( f   =   1   ,   . .   ,   h   &   secific   t   =   2 ,   . . ,   T ) ,   λ jt 1   0   ( j = 1 ,   . .   ,   n   &   t   =   2 ,   . . ,   T   ) ,   ξ k t : URS   ( k :   specific   &   all   t   =   2 ,   . . ,   T ) ,     d ikt   x + 0   ( i = 1 , . . , m )   &   d f k t b 0   ( f = 1 , . . , h ) .
The index measures the performance of the k th DMU at the t th period by comparing itself with the efficiency frontier of the t − 1 period. Therefore, Model (3) considers only observations in t − 1 th period (for making an efficiency frontier) and those of t th periods whose efficiencies are examined by Model (3).
A unified index measure (UIM) of the k th DMU at the t th period is measured by
U I M k t = 1 [ ξ k t +   ε s ( i = 1 m R i x d i k t x + + f = 1 h R f b d f k t b ) ] .
Here, the inefficiency measure and all slack variables are determined on the optimality of Model (3). The degree of unified index is obtained by subtracting the level of inefficiency from unity as specified in Equation (4). In contrast to the efficiency measure (2), the index measure (4) produces the unfired index that may be larger than unity, so showing a technological progress on pollution prevention.
At the end of this section, this study needs to note the three computational concerns on the proposed two approaches. First, we assume constant DTS to avoid computational infeasibility. Second, we understand that the proposed approaches suffer from an occurrence of multiple solutions (e.g., multiple reference sets and multiple supporting hyperplanes). Finally, there is a possibility that an observed data set (e.g., including an outlier) does not fit with the assumption of the null-joint hypothesis incorporated into the two models. In the case, a computer code may produce an infeasible solution. This indicates that the data set does not satisfy the hypothesis, not the ordinary infeasibility on computing linear programming.

5. An Illustrative Example

5.1. Data

For the analytic framework of inputs, desirable outputs, and undesirable outputs, we collected state-level data during the period of 2009 to 2018 from four different sources: (1) population data from the U.S. Census, (2) government expenditure data from the National Association of State Budget Officers, (3) energy consumption data from the U.S. Energy Information Administration, (4) patent data from the U.S. Patent and Trademark Office, (5) gross domestic product data from the U.S. Bureau of Economic Analysis, and (6) carbon dioxide data from the U.S. Environmental Protection Agency.
There were four inputs: (1) population, (2) government expenditure, (3) energy consumption, and (4) patent grants. The first two inputs represent labor and capital while the last two account for material (or resource) and technological feedstock to the production. The population was measured by thousands of people. Government expenditure was measured by U.S. million dollars. The amount of energy consumption was measured by billion BTU. The number of patents was measured at grants. There were one desirable and one undesirable outputs: gross state product (GSP) and carbon emissions. The former represents economic vitality while the latter takes environmental sustainability into account as a byproduct of production. GSP was measured by U.S. $ million. The amount of carbon emissions was measured in million metric tons of CO2.
Table 4 exhibits data sets: part (a) lists 25 blue states where Biden won and part (b) does 25 red states where Trump won in the 2020 presidential election. Coincidentally, the election result is half and half by states. The data includes input and output production factors by states. Instead of enumerating all data from 2009 to 2018, we present 2018 data and descriptive statistics over the past 10 years. States are listed in the alphabetical order of their names. There are 25 blue states in part (a) and 25 red states in part (b). As of 2018, blue states have more populations, government expenditures, patent grants, and economic production than red states do. In contrast, red states use more energy and emit more carbon dioxide. On average, blue states have approximately 7.5 million people, spend $51 billion of the budget, use 1.9 quadrillion BTU of energy, receive 5 thousand patents, generate $511 billion of GSP, and emit 93 million tons of CO2. Meanwhile, red states have approximately 5.6 million people, spend $28 billion of the budget, use 2.2 quadrillion BTU of energy, receive 1.6 thousand patents, generate $303 billion of GSP, and emit 128 million tons of CO2.
Table 5 shows the data statistics of two outputs: GSP and CO2. States’ mean values over 10 years are presented along with standard deviation values in the parenthesis. The descriptive statistics of blue states are presented first and that of red states later. Although some blue and red states have similar sizes of their economies, they emit different levels of CO2. For instance, New York (a blue state) and Texas (a red state) produce $1406 billion and $1480 billion of GSP whereas they emit 170 MMT and 766 MMT of CO2.

5.2. Efficiency/Index Measures

Figure 1 maps states’ mean UEM scores over the past decade. Greener states indicate higher environmental performance while redder ones point out lower performance. States on both coasts tend to outperform those in the middle. Figure 2 and Figure 3 depict mean UEM and UIM scores of blue and red states over time. The mean UEM and UIM scores gaps between blue and red states are obvious but they become slightly wider in the mean UEM (the environmental performance of blue states has improved more than that of red states has) while becoming narrower in the mean UIM (the environmental performance of blue states has stagnated whereas that of red states has enhanced). One notable thing is that mean UEM and UIM scores both tend to increase since 2009 but they started to decrease or level off from 2017. Although it requires more data (in 2019 and 2020) to confirm, the possible reason may be the political change from the Obama Administration to the Trump Administration.
Table 6 and Table 7 summarize UEM and UIM scores and ranks of blue and red states over time. They are results of Models (1) and (3) estimations for efficiency measures. As of 2018, the UEM and UIM scores of blue states (0.819 and 0.836) are higher than those of red states (0.642 and 0.700). The top five states include California, Massachusetts, New York, Oregon, and Washington, all of which are located on both coasts and are politically liberal. Over the past decade, the UEM score of blue states has increased by 13.75% while that of red states has increased by 8.08%. However, the UIM score of blue states has slightly decreased (−0.36%) whereas that of red states has increased by 16.47%. It implies that (a) overall blue states outperform red states, (b) blue states’ environmental performance has improved more than red states has, and (c) red states’ technological progress has been made faster than blue states’ has. Both environmental performance and technological progress declined between 2017 and 2018: from 0.822 to 0.819 and from 0.860 to 0.836 in blue states and from 0.644 to 0.642 and from 0.723 to 0.700 in red states, respectively.

5.3. Statistical Test

To examine our hypotheses, we graphically describe differences in UEM scores and conducted the Kruskal-Wallis tests of UEM/UIM scores among different groups of states. Specifically, panels (a) and (b) of Figure 4 demonstrate mean UEM scores across states with different political transitions in presidential and gubernatorial elections. In the former, states with D to D transition outperformed their counterparts (i.e., states with D to R or R to R transition). It is noted that there was no state with R to D transition in the presidential election. In the latter, states with R to D or D to D outperformed their counterparts. Interestingly, states with R to D transition performed the best even if they are compared to states with D to D. On one hand, it implies that some Republican governors (particularly, those in blue states) committed to environmental protection or climate actions. They include California, Connecticut, Hawaii, Minnesota, and so forth. On the other hand, states with D to R transition improved faster than those with R to R transition. Even though political hegemony was shifted to the Republican from the Democratic in those states, it seems that the learning curve from the Democratic gubernatorial administration may influence the following Republican administration. States with R to R transitions performed the worst and their mean UEM scores stagnated.
Panels (c) and (d) of Figure 4 demonstrate regional variations in mean UEM scores. It is clear that Seattle and Philadelphia regions (defined by EDA) and Regions 1, 2, and 10 (defined by EPA) outperformed their counterparts. They include Pacific Northwest (e.g., Oregon and Washington) and New England states (e.g., Massachusetts and New York). EDA’s Denver region, which is composed of EPA’s Regions 8 and 9, underperformed other regions. While EPA’s Region 9 (e.g., California and Hawaii) performed well, Region 8 (e.g., North and South Dakotas) performed poorly.
Table 8 and Table 9 summarize the results of the Kruskal-Wallis tests vis-à-vis a political context (hypothesis 1). Chi-squares (χ2) statistics indicate that we can reject null hypotheses of identical mean UEM/UIM scores among three or four groups of states with different presidential or gubernatorial election results. The UEM/UIM scores of D to D or R to D groups are statistically significantly higher than those of D to R or R to R groups. Table 10 and Table 11 summarize the results of the Kruskal-Wallis tests regarding a spatial context (hypothesis 2). The χ2-statistics indicate that we can reject null hypotheses of identical mean UEM/UIM scores among six or ten groups of states situated in different EDA or EPA regions. The UEM/UIM scores of Seattle and Philadelphia regions or Regions 1, 2, and 10 are statistically significantly higher than those of other regions.

5.4. Results and Discussion

This study demonstrated the variations of the environmental performance of 50 states of the U.S. by temporal, political, and spatial contexts. The summarized results are as follows. Temporally, the environmental performance of states tends to have improved regardless of their political transitions and locations. Politically, the UEM/UIM scores of blue states have been significantly higher than those of red states, suggesting that the overall environmental performance is better in blue states than in red states. Meanwhile, it is worth noting that red states’ technological progress is substantial. It was dramatic particularly in the states with the political transition from D to R, implying that even though there was a political hegemony change in those states, climate/environmental learning from the previous Democratic administration may have some impacts on their residents/public opinions and the following Republican administration. Geographically, the Pacific Northwest and New England regions (Seattle and Philadelphia regions defined by EDA and Regions 1, 2, and 10 defined by EPA) demonstrated better environmental performance than their counterparts.
To some degree, states with the political transition from D to D or from R to D overlap those on both coasts. However, it does not explain everything. Over the past decade, on one hand, political partisanship has transitioned from D to R, particularly in the presidential elections where there were eight states with the political transition from D to R but no state with the political transition from R to D. On the other hand, more and more states (or governors) have committed to climate/environmental policy regardless of their dominant political partisanship. For instance, Montana had the political transition from R to R in the presidential elections but signed up for the U.S. Climate Alliance to meet the goals proposed by the Paris Agreement. In some states, in addition, there is discordance in political transition between presidential and gubernatorial elections. For example, Vermont had the political transition from D to D in the presidential elections but from R to R in the gubernatorial elections.
While many studies (mostly focused on cross-country analyses) examined the relationships between environmental performance and socioeconomic factors (suggested by the environmental Kuznets Curve), this study centered on the examination of temporal, political, and spatial factors in the U.S. When compared to some studies in this vein, our results showed some consistent results. For instance, Leal et al. [30] demonstrated the association between environmental performance and political globalization that represents the dissemination and sharing of government environmental policies. Also, Esty and Porter [31] showed the relationship between environmental performance and the quality of the environmental regulations (particularly, the rigor and structure of enforcement). They further argued that environmental performance is related to a country’s broader institutions (not only environmental regulatory regime but also economic and legal context). As shown in the previous literature, the political dimension interplays with the establishment and elimination of national institutions (particularly, environmental regulatory regime), which can lead to improvement or degradation of environmental performance. Our study reinforces the previous results and offers new policy insights in that we studied the dynamic political transition, not static political section at a specific time point.
Regional variations in environmental performance in this study are also bolstered by the existing literature whose studies were conducted in different countries. For instance, Zuo et al. [32] showed province-level variations in environmental performance in China by creating a composite index. In a similar vein, Yang and Yang [33] demonstrated province-level differences in eco-innovations in China, which are critical for environmental performance, by employing a non-radial directional distance function.

6. Conclusions

Drawing on the combination of the nonparametric DEA-EA with Kruskal-Wallis tests, we attempted to assess the dynamic environmental performance of 50 states of the U.S. and shed light on their associations with political/spatial contexts. As a result, we found that (a) overall environmental performance has gradually enhanced over time, (b) there were statistically significant differences in the environmental performance by political transitions, and (c) states on both coasts outperformed those in the middle.
Meanwhile, we need to acknowledge that those contexts are a subset of all explanatory factors for state-level environmental performance. As some studies discussed, there may be other dimensions, such as public and local government leader opinions [34], religion [35], and the structure of the energy market [36,37], in explicating environmentalism.
There are some other limitations in this study. First, we considered only two outputs (i.e., GSP and CO2 emissions) to assess the performance of states in terms of sustainable development. While CO2 attracts the most attention in climate change, other greenhouse gases (e.g., methane) and pollutants (e.g., SOx and NOx) were omitted. Second, our study period was not sufficient to fully capture the change from the Obama Administration to the Trump Administration. While we included data from a part of the Trump Administration (2017–2018) and observed some decline in the environmental performance in the time window, we would be able to argue better if we could extend our dataset up to 2020. However, it was not possible due to the data availability issue. Finally, it is possible for us to use other type of DEA methods such as the “intermediate approach” [38]. The methodological comparison may avoid a methodological bias. It is hoped that our future studies will address those limitations. See [39,40,41,42] for a general direction on DEA applied to energy and environment.
As it is anticipated that the Biden Administration steps in, the public and political leaders in the U.S. will face different national mood and focusing events from the Trump Administration in the near future. The Biden Plan for a Clean Energy Revolution and Environmental Justice includes many pro-environmental ideas such as the Green New Deal and recommitment to the Paris Agreement [43]. In particular, the incoming administration proposes to “achieve a 100% clean energy economy and reach net-zero emissions no later than 2050.” With the promise, we expect the states’ environmental performance to keep improving.

Author Contributions

Conceptualization, T.S. and Y.R.; methodology, T.S.; software, Y.R.; validation, T.S. and Y.R.; formal analysis, T.S.; investigation, Y.R.; resources, Y.R.; data curation, Y.R.; writing—original draft preparation, Y.R.; writing—review and editing, T.S.; visualization, Y.R.; supervision, T.S.; project administration, Y.R.; funding acquisition, Y.R. All authors have read and agreed to the published version of the manuscript.


This research was funded by the U.S. Department of Defense; the grant number HQ0034-19-FOA-ARP-0001.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


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Figure 1. Average Unified Efficiency Scores of Fifty States over Period of 2009–2018.
Figure 1. Average Unified Efficiency Scores of Fifty States over Period of 2009–2018.
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Figure 2. Average Unified Efficiency Scores of Blue and Red States from 2009–2018.
Figure 2. Average Unified Efficiency Scores of Blue and Red States from 2009–2018.
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Figure 3. Average Unified Index Scores of Blue and Red States from 2009–2018.
Figure 3. Average Unified Index Scores of Blue and Red States from 2009–2018.
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Figure 4. UEM Scores over Time by States (a) with History of Presidential Elections Transitioning from the Democratic to the Democratic (D to D, Blue-Colored), from the Democratic to the Republican (D to R, Orange-Colored), and from the Republican to the Republican (R to R, Red-Colored), (b) with History of Gubernatorial Elections Transitioning D to D (Blue-Colored), R to D (Cyan-Colored), D to R (Orange-Colored), and R to R (Red-Colored), (c) of EDA’s Six Different Regions, and (d) of EPA’s Ten Different Regions.
Figure 4. UEM Scores over Time by States (a) with History of Presidential Elections Transitioning from the Democratic to the Democratic (D to D, Blue-Colored), from the Democratic to the Republican (D to R, Orange-Colored), and from the Republican to the Republican (R to R, Red-Colored), (b) with History of Gubernatorial Elections Transitioning D to D (Blue-Colored), R to D (Cyan-Colored), D to R (Orange-Colored), and R to R (Red-Colored), (c) of EDA’s Six Different Regions, and (d) of EPA’s Ten Different Regions.
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Table 1. Previous Assessments on Performance of U.S. States.
Table 1. Previous Assessments on Performance of U.S. States.
Yilmaz & Dinc [10]Conventional DEAThis study explored 48 states’ telecommunications infrastructure use performance over the period of 1984–1997.Private, public, and telecommunications capital stocks and laborTotal value added of states’ private industries
Lee & Joo [11]Conventional DEAThis study examined 50 states’ operational performance of correctional facilities in 2005.Capacity and expenditureNumber of inmates and recidivism
Khan & Murova [12]Conventional DEAThis study looked into 50 states’ operational performance over the period of 1992–2012.State expenditure, employment, and populationGross state product
Thomas et al. [13]Efficiency ratioThis study shed light on changes of 50 states’ R&D efficiency ratios between 2004 and 2008. R&D expenditurePatents granted and scientific publications
Drivas et al. [14]SFAThis study measured 50 states’ output and knowledge production efficiencies over the period of 1993–2006.Gross capital stock, the number of workers, business R&D stock, and the number of scientists and engineersGross state product and patents
Gearhart [15]Hyperbolic order-α estimatorThis study analyzed 50 states’ health care efficiency over the period of 2002–2008.Vaccine, citizens/hospital, inpatient days, hospital beds, cost, etc.Infant/teen survival rate and life expectancy
Gearhart & Michieka [16]Conditional order-m estimatorThis study analyzed 50 states’ health care efficiency over the period of 2014–2017.Healthcare costs and the fraction of individuals with some collegeYears of life gained and the fraction of infants born normal birthweight
Park et al. [17]Non-radial SBM-DEAThis study evaluated 50 states’ environmental performance in the transportation sector over the period of 2004–2012.Capital expense, energy consumption and labor in the transportation sectorTransportation value added and CO2 emissions
Halkos & Polemis [18]Window DEAThis study assessed 50 states’ environmental efficiency in the power generation sector over the period of 2000–2012.Total energy transmission and total operating costUse of net capacity, CO2, SO2 and NOx emissions
Table 2. States by Results of Presidential and Gubernatorial Elections.
Table 2. States by Results of Presidential and Gubernatorial Elections.
AlabamaRRRR to RRRRRR to RMontanaRRRR to RDDDD to D
AlaskaRRRR to RRRIRR to RNebraskaRRRR to RRRRRR to R
ArizonaRRRR to RDRRRD to RNevadaDDDD to DRRRDR to D
ArkansasRRRR to RDDRRD to RNew HampshireDDDD to DDDRRD to R
CaliforniaDDDD to DRDDDR to DNew JerseyDDDD to DDRRDD to D
ColoradoDDDD to DDDDDD to DNew MexicoDDDD to DDRRDD to D
ConnecticutDDDD to DRDDR to DNew YorkDDDD to DDDDDD to D
DelawareDDDD to DDDDD to DN. CarolinaDRRD to RDRDD to D
FloridaDDRD to RRRRRR to RN. DakotaRRRR to RRRRR to R
GeorgiaRRRR to RRRRRR to ROhioDDRD to RDRRRD to R
HawaiiDDDD to DRDDDR to DOklahomaRRRR to RDRRRD to R
IdahoRRRR to RRRRRR to ROregonDDDD to DDDDDD to D
IllinoisDDDD to DDDRDD to DPennsylvaniaDDRD to RDRDDD to D
IndianaDRRD to RRRRR to RRhode IslandDDDD to DRIDDR to D
IowaDDRD to RDRRRD to RS. CarolinaRRRR to RRRRRR to R
KansasRRRR to RDRRDD to DS. DakotaRRRR to RRRRRR to R
KentuckyRRRR to RDDRD to RTennesseeRRRR to RDRRRD to R
LouisianaRRRR to RRRDR to DTexasRRRR to RRRRRR to R
MaineDDDD to DDRRDD to DUtahRRRR to RRRRR to R
MarylandDDDD to DDDRRD to RVermontDDDD to DRDDDRRR to R
MassachusettsDDDD to DDDRRD to RVirginiaDDDD to DDRDDD to D
MichiganDDRD to RDRRDD to DWashingtonDDDD to DDDDD to D
MinnesotaDDDD to DRDDDR to DW. VirginiaRRRR to RDDDD to D
MississippiRRRR to RRRRR to RWisconsinDDRD to RDRRDD to D
MissouriRRRR to RDDRD to RWyomingRRRR to RDRRRD to R
Note: D = Democratic, R = Republican, and I = Independent. For instance, DDD or DDDD means Democratic candidates have won three or four elections in a row over the past decade (e.g., Colorado and New York). RRR or RRRR means Republican candidates have won three or four elections in a row (e.g., Alabama and South Dakota). DDR or DRRR represents the political transition from the Democratic to Republican Party (e.g., Florida in the Presidential elections and Wyoming in the Gubernatorial elections). RDDD or RRRD presents the political transition from the Republican to Democratic Party (e.g., California and Nevada in the Gubernatorial elections).
Table 3. States by EDA and EPA Regions.
Table 3. States by EDA and EPA Regions.
EDA RegionEPA RegionState
Regional Office
Region 1Connecticut
New Hampshire
Rhode Island
Region 2New Jersey
New York
Region 3Delaware
West Virginia
Regional Office
Region 4Alabama
North Carolina
South Carolina
Chicago Regional OfficeRegion 5Illinois
Regional Office
Region 6Arkansas
New Mexico
Region 7Iowa
Regional Office
Region 8Colorado
North Dakota
South Dakota
Region 9Arizona
Regional Office
Region 10Alaska
Note: EDA = Economic Development Administration and EPA = Environmental Protection Agency.
Table 4. Production Factors Data of (a) 25 Blue States and (b) 25 Red States in 2018.
Table 4. Production Factors Data of (a) 25 Blue States and (b) 25 Red States in 2018.
($ Million)
Energy Consump.
(Billion BTU)
($ Million)
New Hampshire13496131324,69399884,58415
New Jersey888660,7752,240,7094682612,979111
New Mexico209320,402702,827535100,08047
New York19,530163,7443,854,18497801,705,010172
Rhode Island10589262197,37741559,92512
N. Carolina10,38247,7952,616,1333781567,452122
N. Dakota7585889660,95912356,28755
S. Carolina508425,2571,671,7811142235,28775
S. Dakota8794457396,83715753,23916
W. Virginia180416,857832,91415277,63389
Table 5. Descriptive Statistics of 50 States’ Production Factors Data from 2009–2018.
Table 5. Descriptive Statistics of 50 States’ Production Factors Data from 2009–2018.
($ Million)
($ Million)
($ Million)
Arizona287,02393New York1,405,735170Louisiana230,649253
(35,701)(3) (181,537)(5) (11,677)(8)
(369,901)(6) (28,022)(1) (6464)(4)
(41,370)(3) (66,497)(13) (22,228)(6)
Connecticut253,02836Rhode Island53,83811Montana43,51333
(15,974)(1) (4087)(1) (4480)(1)
(6038)(1) (2236)(0) (12,431)(2)
Georgia486,445145Virginia465,731105N. Carolina475,184127
(67,949)(14) (40,253)(3)(55,673)(7)
Hawaii78,31719Washington443,09978N. Dakota48,81653
(9248)(0) (73,307)(4)(9093)(3)
(72,389)(11) (30,245)(3) (64,807)(15)
(4738)(1) (16,091)(7) (16,887)(4)
Maryland354,46963Alaska53,55438S. Carolina192,02776
(36,743)(5) (3003)(2)(26,110)(5)
Massachusetts473,34267Arkansas112,73666S. Dakota45,05615
(58,661)(3) (9882)(4)(5378)(0)
(51,995)(5) (114,589)(6) (38,863)(4)
(36,274)(2) (8347)(1) (195,335)(41)
(16,208)(2) (33,301)(13) (22,173)(3)
New Hampshire72,07315Iowa165,01884W. Virginia70,02093
(7637)(1) (18,500)(5)(4009)(4)
New Jersey543,682113Kansas146,99968Wyoming38,02965
(43,957)(4) (15,432)(5) (1407)(2)
New Mexico89,85753Kentucky183,293137
(5267)(4) (16,538)(12)
Note: Standard deviation in the parenthesis.
Table 6. Unified Efficiency Scores of (a) Blue States and (b) Red States from 2009–2018.
Table 6. Unified Efficiency Scores of (a) Blue States and (b) Red States from 2009–2018.
New Hampshire0.678(21)0.698(19)0.712(19)0.789(17)0.822(13)0.791(17)0.790(17)0.867(12)0.891(12)0.872(12)
New Jersey0.769(15)0.768(17)0.765(18)0.802(14)0.803(17)0.781(18)0.783(18)0.780(17)0.813(17)0.808(18)
New Mexico0.395(47)0.428(45)0.417(46)0.431(46)0.438(45)0.475(44)0.477(45)0.496(45)0.499(45)0.552(42)
New York0.939(3)0.934(4)0.996(4)1.000(1)1.000(1)0.954(7)0.958(5)0.971(4)1.000(1)1.000(1)
Rhode Island0.812(11)0.818(10)0.824(11)0.858(9)0.893(8)0.861(9)0.820(15)0.943(7)0.905(11)0.812(16)
N. Carolina0.691(19)0.664(22)0.710(20)0.737(19)0.728(22)0.730(20)0.765(20)0.771(18)0.791(19)0.802(20)
N. Dakota0.291(49)0.314(48)0.337(48)0.347(48)0.360(48)0.378(48)0.370(48)0.373(48)0.390(48)0.393(48)
S. Carolina0.624(25)0.633(24)0.656(25)0.695(24)0.742(19)0.723(21)0.744(23)0.757(20)0.781(21)0.759(23)
S. Dakota0.764(16)0.774(15)0.817(12)0.791(16)0.807(16)0.815(14)0.866(11)0.833(13)0.851(14)0.834(15)
W. Virginia0.310(48)0.275(49)0.276(50)0.294(49)0.295(49)0.269(49)0.287(49)0.278(49)0.290(49)0.320(49)
Note: Rank in the parenthesis.
Table 7. Unified Index Scores of (a) Blue States and (b) Red States from 2009–2018.
Table 7. Unified Index Scores of (a) Blue States and (b) Red States from 2009–2018.
New Hampshire0.822(16)0.792(19)0.828(15)0.847(16)0.834(18)0.828(19)0.919(12)0.932(11)0.894(13)
New Jersey0.863(14)0.850(14)0.857(13)0.843(17)0.827(20)0.827(20)0.824(18)0.853(19)0.820(20)
New Mexico0.480(44)0.458(45)0.449(45)0.459(46)0.519(43)0.503(45)0.526(45)0.524(46)0.565(43)
New York1.062(3)1.087(1)1.051(4)1.019(4)1.001(8)1.037(2)1.053(1)1.067(2)1.001(5)
Rhode Island0.998(9)0.922(10)0.881(11)0.909(13)0.990(9)0.886(14)0.991(6)0.927(12)0.817(21)
N. Carolina0.728(22)0.776(20)0.767(19)0.754(22)0.772(22)0.803(21)0.822(19)0.838(21)0.824(19)
N. Dakota0.321(49)0.350(48)0.349(48)0.362(49)0.398(48)0.381(48)0.402(48)0.422(48)0.417(48)
S. Carolina0.653(28)0.686(29)0.698(26)0.746(24)0.758(24)0.765(26)0.809(22)0.836(22)0.795(23)
S. Dakota0.787(18)0.846(16)0.795(18)0.810(20)0.854(16)0.889(13)0.892(13)0.913(15)0.876(14)
W. Virginia0.451(46)0.305(49)0.295(49)0.980(8)0.294(49)0.300(49)0.298(50)0.308(50)0.338(50)
Note: Rank in the parenthesis.
Table 8. Kruskal-Wallis Tests of States with Different Presidential Election Results.
Table 8. Kruskal-Wallis Tests of States with Different Presidential Election Results.
Efficiency/IndexMean (Rank Sum)χ2-Statistic
D to DD to RR to R
UEM0.816 (70,082)0.638 (16,747)0.586 (38,421)162.777 ***
UIM0.874 (56,885)0.672 (12,803)0.634 (31,787)146.295 ***
Note: *** = significant at 1%.
Table 9. Kruskal-Wallis Tests of States with Different Gubernatorial Election Results.
Table 9. Kruskal-Wallis Tests of States with Different Gubernatorial Election Results.
Efficiency/IndexMean (Rank Sum)χ2-Statistic
D to DD to RR to DR to R
UEM0.695 (44,269)0.628 (24,458)0.817 (24,888)0.660 (31,635)54.365 ***
UIM0.742 (35,251)0.671 (19,723)0.887 (20,447)0.710 (26,055)51.119 ***
Note: *** = significant at 1%.
Table 10. Kruskal-Wallis Tests of States of EDA’s Six Different Regions.
Table 10. Kruskal-Wallis Tests of States of EDA’s Six Different Regions.
Efficiency/IndexMean (Rank Sum)χ2-Statistic
UEM0.664 (18,442)0.587 (14,492)0.625 (11,997)0.600 (19,154)0.802
0.887 (16,036)161.557 ***
UIM0.711 (14,939)0.625 (11,437)0.659 (9112)0.639 (15,285)0.869
0.966 (13,482)168.804 ***
Note: *** = significant at 1%.
Table 11. Kruskal-Wallis Tests of States of EPA’s Ten Different Regions.
Table 11. Kruskal-Wallis Tests of States of EPA’s Ten Different Regions.
Efficiency/IndexMean (Rank Sum)χ2-Statistic
Region 1Region 2Region 3Region 4Region 5Region 6Region 7Region 8Region 9Region 10
UEM0.880 (24,095)0.884 (8041)0.674 (12,993)0.664 (18,442)0.625 (11,997)0.600 (8629)0.571 (5863)0.497 (7107)0.753 (12,047)0.887 (16,036)230.318 ***
UIM0.955 (19,904)0.941 (6516)0.738 (10,801)0.711 (14,939)0.659 (9112)0.646 (7045)0.598 (4392)0.525 (5474)0.810 (9811)0.966 (13,482)234.047 ***
Note: *** = significant at 1%.
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