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American Journal of  Economics and 
Business Innovation (AJEBI)

Analyzing the Long-Term Impact of  U.S. Federal Sustainability Programs Using 
Business Intelligence

Morium Akter1, Ismot Zerine2, Md Mainul Islam2*, Md Rakibul Haque Pranto3, Tauhedur Rahman4

Volume 3 Issue 1, Year 2024
ISSN: 2831-5588 (Online), 2832-4862 (Print)

DOI: https://doi.org/10.54536/ajebi.v3i1.5690
https://journals.e-palli.com/home/index.php/ajebi

Article Information ABSTRACT

Received: February 15, 2024

Accepted: April 07, 2024

Published: April 10, 2024

The research is important to fill a critical gap and the potential contribution of  business 
intelligence (BI) tools to improve program impacts. Although there are available studies 
that have discussed the presence of  short-term outcomes, little has been said about the 
long-term effects of  such initiatives, particularly using data-driven tools. Observing long 
run environmental, economic and social impacts of  federal sustainability programs is also 
very important in enhancing future decision making and optimal allocation of  resources. 
The main task of  this study was to evaluate the charge of  federal sustainability programs 
on the lowered emissions, decreased money savings, and energy efficiency as well as address 
the role of  BI tools. The longitudinal approach adopted in the study utilized data covering 
more than 50 federal sustainability programs in the 5-12 year data span. To assess the role of  
program characteristics in the relationship with its outcomes, the statistical methods based 
on Pearson correlation, multiple regression, T-tests, ANOVA, and Chi-Square tests were 
used. Important findings were the programs that involved the use of  BI tools had continued 
evidence that they have greater emission reductions (500,000 vs. 300,000 tons CO2 ) and cost 
savings (USD 5.5 million vs. USD 3 million). Program duration was also positively correlated 
with emissions reduction, and longer programs (approximations of  10 and 12 years) had 
larger reductions. The Chi-Square test also showed that BI tools usage is strongly associated 
with program success since programs utilizing BI tools have an increased chance of  having 
more than $5 million in cost savings (p = 0.015). This study not only demonstrated the use 
of  BI in sustainability initiatives but also had important implications for streamlining future 
federal sustainability programs

Keywords
Business Intelligence, Federal 
Sustainability Programs, Reduction 
of  Emissions, Statistical Analysis, 
Success of  The Programs

1 Department of  Information Technology and Project Planning Management. ST. Francis College, USA
2 College of  Graduate and Professional Studies, Trine University, USA 
3 Department of  Management, ST. Francis College, USA
4 Department of  Dahlkemper School of  Business, Gannon University, USA
* Corresponding author’s e-mail: islammainul2019@gmail.com

INTRODUCTION
Sustainability is the topic around which the public 
policy and the strategy of  the private sphere are built, 
and governments and organizations strive to approach 
sustainability by addressing issues with the environment, 
economic stability, and social health (Hariram et al., 2023). 
Federal sustainability programs in the United States stand 
among the most prominent initiatives opposing these 
issues to achieve environment-friendly behavior and 
improve energy efficiency, decrease carbon emissions, 
and advance the economic development of  the country 
(Ukpoju et al., 2024). Such programs agreed upon in 
different spheres, like energy, agriculture, transportation, 
and waste management, are a serious step by the federal 
government into solving urgent environmental and 
social concerns (Shan & Ji, 2024). Nonetheless, even 
though they are considered ambitious, little has been 
written about the long-term outcome of  these initiatives 
especially in the eyes of  business intelligence tools that 
would give profound insights regarding their long-term 
performance measure. The present study, namely, the 
examination of  the long-term effectiveness of  U.S. federal 
sustainability programs through business intelligence, will 
attempt to eliminate this gap by utilizing the power of  

data to measure the effectiveness of  various sustainability 
programs along different parameters. This paper can 
help determine highly valued information regarding 
federal policy sustainability and the overall effects on 
the environmental, economy, and social system with the 
help of  business intelligence (BI) tools and techniques, 
including data mining, predictive analytics, and regression 
analysis, among others (Adewusi et al., 2024).

Background
The United States has been a global leader in developing 
and implementing sustainability programs, with federal 
programs dating back to the 1970s. The initial policy 
laid on cutting down air and water pollution, was 
unable to manage waste better, and to utilize natural 
resources (Anastas & Zimmerman, 2021). These 
programs have changed over the decades, and nowadays 
one will focus more on adoption of  renewable energy, 
strategies to reduce greenhouse gas emissions, and more 
comprehensive sustainability strategies. Nowadays, the 
range of  U.S. federal programs to support sustainability 
extends to different sectors, and some of  them, like 
Energy Independence and Security Act of  2007, Clean 
Power Plan, Green New Deal are heavily promoted in 



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media as some game-changers in terms of  environmental 
issues (Klass et al., 2022; Kalogiannidis et al., 2024). 
Although an extensive amount of  resources has been 
dedicated to these efforts, the long-term impact of  these 
initiatives has proven to be a difficult task because the 
factors impacting them are complex and evaluating them 
on a long-term scale is challenging (Lada et al. 2023). 
The possibility of  business intelligence being used to 
deliver business intelligence and predictive analysis can 
be a profitable entry point in breaking these barriers and 
understanding the real effects of  sustainability action 
(Paramesha et al., 2024). The focus of  this study is local as 
well as international. At the local level, the study centers 
on the U.S. federal sustainable policies and their direct 
and indirect influence on the environmental policies, 
business strategies and the socio-economic development 
in the country (Hariram et al., 2023). The findings will 
help in constructing the global discussion on sustainable 
development internationally as they will give relevant 
information that can be put to test in other countries 
with the same programs. The undertaken comparative 
review of  the U.S. federal sustainability programs cross-
referenced with international equivalents can assist in 
determining best practices, areas of  improvement, and 
a blueprint of  the capability to analyze sustainability 
programs among countries (Wren, 2022).
The literature has presented many studies concerning 
different components of  sustainability scheme and 
majority of  the research has considered both short 
term and qualitative impacts. As an example, a number 
of  publications concerning environmental impact 
assessment quite frequently point out the effectiveness 
of  specific programs, with few papers providing a long-
term overview at the overall scale. As (Kramar, 2022) and 
Awewomom et al., (2024) among other scholars would 
point out, there are a number of  calls to shift towards 
bringing more in-depth evaluations, which would not 
only gauge the environment-related benefits but would 
also take into account the wider economic and social 
effects. Lee and Choi (2019), on the contrary, commented 
on the necessity to apply advanced data analytics to 
monitor the sustainability performance, where business 
intelligence tools may play a major role in sustainability 
programs monitoring and assessment (Simon et al., 2024). 
Nevertheless, regardless of  the great prospects of  BI tools, 
a scarcity of  research studies integrating these technologies 
into a long-term perspective on the regulation of  the 
federal sustainability programs can be observed.
Further, research on the usefulness of  business intelligence 
in the environmental policy research is in its infancy. As 
per the recent research by Mohamed et al. (2024) and (Li 
& Lakzi, 2022), the mechanism of  using BI tools in the 
energy sector to enhance the process of  decision-making 
concerning sustainability was discussed. The incorporation 
of  BI in an assessment of  American federal sustainability 
projects, namely those that look at long term durations 
is wanting. It is due to this gap that the current study is 
particularly novel because it is incorporating the concepts 

of  sustainability and business intelligence in order to 
implement a longitudinal assessment of  federal efforts 
(Chalmeta & Ferrer , 2023). Such a study is absolutely 
crucial in two ways. To begin with, this research provides 
a new strategy in assessing federal sustainability programs 
based on business intelligence tools and removes the 
methodological flaws of  the prior literature (Ferrer, 
2024). Second, the findings will add value to the body 
of  knowledge in terms of  sustainability studies as well 
as business intelligence in providing important insights 
to policymakers, environmentalists, and business leaders 
on the effectiveness of  the federal programs. As the 
U.S. is trimming its sustainability strategies against the 
background of  climate change and resource depletion, 
the long-term effects of  the given programs are necessary 
to define the future policy decision makers and to make 
the given resources efficiently spendable in the future (Lu 
& Wang, 2023).
This first area of  motivation of  the research is the 
growing awareness of  the necessity to assess the long-
term implications of  sustainability programs. Although 
the importance of  short-term evaluations cannot be 
underestimated, they do not reflect the bigger and more 
intricate impact that federal programs may have on the 
sustainability development of  a country (Biermann et al., 
2022). In addition, more is required to have a big data, 
which can guide policymaking and enable changes to 
be made during a program to ensure that they are more 
responsive. To address this need, business intelligence, 
and its higher, data analytics capabilities, provides a very 
strong resource. This study can clarify that there is an 
essential gap in the knowledge about the performance 
of  the U.S. federal sustainability programs regarding the 
time and the data collection that may help to enhance 
future sustainability program enhancement (Adewusi et 
al., 2024).

Research Gap
The identified research gap revealed in this study can be 
described by the absence of  such longitudinal assessment 
of  the U.S. federal sustainability programs that implement 
the use of  business intelligence tools to process data 
properly (Okaily et al., 2023). The majority of  the best 
existing studies do it either through concentrated 
studies of  the short-term effects or qualitative analysis 
without employing statistics in calculating the long-
term effectiveness of  federal programs. In addition, 
presently there is an increasing interest over the use of  
BI concerning environmental and energy management, 
but there was no research conducted on how BI can 
be applied to federal sustainability programs (Simon et 
al., 2024). It is this gap that this study seeks to bridge 
by using the BI tools to very rigorously and data-driven 
review these programs on the basis of  the environmental, 
economic, and social impacts over time.

Research Questions 
The major research question, which this study attempts 



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to answer is; In what ways have federal sustainability 
programs in the U.S. affected the environment, economy 
and social impacts in the long run, and to what degree 
are business intelligence tools able to give actionable 
insight into the impact of  these programs? The research 
design applied in the study is descriptive and correlational 
study utilizing past data of  the U.S. federal sustainability 
programs. Large datasets will be analyzed using business 
intelligence tools, e.g. Tableau, and R, with particular 
attention to the indicators comprising key sustainability 
measurements including reductions of  emissions, 
energy efficiency, economic growth, and adherence to 
policy. Regression analysis will also be incorporated in 
the study to compare all correlations between program 
implementation and the long-term outcomes.

Objectives
The objectives of  the research are mainly the following:
• To determine the long-term environmental effects of  

U.S. federal sustainability programs.
• To assess the success of  intelligence tools in tracking 

and analysis of  performance of  such programs.
• To arrive at barriers and enablers that influence success 

of  federal sustainability efforts.
The research methodology supports these objectives as 
business intelligence has been applied along with statistical 
analysis in gauging the effects of  federal sustainability 
programs over a period. The study was also focused on 
addressing a substantial gap in the field of  sustainability 
and assessing U.S. federal sustainability programs, such as 
through business intelligence tools. Through the inclusion 
of  environmental, economic, and social indicators, the 
study enables a clear picture of  the degree to which such 
programs have led the way in terms of  performance 
over a period of  time to be obtained and understanding 
to be obtained that can inform sustainability initiatives 
in future endeavors. In sum, the proposed study aims to 
impact the larger body of  work on sustainability research 
through the definitive statement showing the influence of  
data analysis in the policy-making process and program 
performance. Not only is this study potentially filling a 
much-needed gap in the sustainability research literature, 
it is also an example of  how future research that attempts 
to achieve an advanced level of  data analysis and policy 
examination can be successfully conducted.

MATERIALS AND METHODS
The main goals of  this study were to assess the 
effectiveness of  U.S. federal sustainability programs, 
discuss the value of  business intelligence tools as the 
measurement device of  the programs, and define the 
most likely causes that condition the success or failure 
of  programs studied. Particularly, the goal of  the study 
was to examine the patterns of  energy use, emission 
decreasing, and resource management to define the 
efficiency of  such measures on the environment. It 
also aimed at determining the ability of  data-driven 
technology, including predictive analytics, data mining, 

etc., to institute program-evaluation and forecasting. 
Moreover, the study explored political, economic, and 
technological facilitators and constraints that determine 
the nature of  sustainability implementation and its results 
on the federal stage. Cumulatively, such goals were aimed 
at painting a complete picture of  the tangible outcomes 
and dynamics of  the federal sustainability programs.

Research Design
Descriptive and correlational research design was used in 
the study since the research objective was to be able to 
describe and quantify the effects of  federal sustainability 
programs and to determine relationship between the 
sustainability programs and different outcome variables. 
The descriptive design made it possible to go into 
the details of  the investigation into the data, and the 
correlational factor assisted in determining whether there 
were any important correlations between the execution 
of  such programs and quantifiable indicators related to 
sustainability. The descriptive design was selected because 
it appeared the most appropriate one in informing about 
the detailed analysis of  the current effects of  sustainability 
programs, and investigation into the complex data sets 
produced by the business intelligence tools. This method 
enabled an analysis with less likelihood of  confounding 
factors and more ethical and a practical option with the 
available information due to the possibility of  correlating 
the program implementation to the performance variables 
although the performance variables could not manipulated.

Study Parameters
Sampling Strategy: The population in the research was 
the U.S. federal sustainability programs realized in the 
last twenty years that encompassed various sectors such 
as energy, agriculture, and transportation. The data 
utilized were obtained using the publicly available federal 
databases, sustainability reports, and business intelligence 
systems accessed by the agencies of  interest including 
the Environmental Protection Agency (EPA) and the 
Department of  Energy (DOE).
Sampling Method- Purposive method of  sampling was used 
whereby sampling was done to include federal programs 
with large data records to analyze, which made the sample 
give an overview of  federal sustainability initiatives.
Sample Size: The paper considered information at least 
10 years. The reasoning behind such sample size was 
informed by prior research whose sample sizes were of  
comparable datasets in federal sustainability studies.
Inclusion/Exclusion Criteria: The programs that were 
only included were the programs that had publicly 
available performance measures and primarily target a 
sustainability impact (environmental, economic, or social). 
Those programs that had no measurable outcomes or 
inadequate data to be analyzed were omitted.

Data Collection 
Instruments
The business intelligence software was the main tool used 



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to collect data as it assisted in extracting, cleaning and 
analyzing large volumes of  data existing in the federal 
sustainability programs. They included Tableau, Power BI, 
and R, and those tools allowed analyzing the data based 
on trends, patterns, and correlation in the environmental 
impact measurements. Also, secondary sources of  data 
based on publicly available reports and databases were 
employed.

Procedure
Data collection procedure encompassed major factors 
as follows: (1) retrieval of  past data of  the federal 
sustainability reports and both performance databases, 
(2) cleansing and processing the information to make it 
more consistent, (3) use of  business intelligence tools 
to perform trend and association analysis, and (4) a 
comparative assessment of  the program performance 
prior to implementation and during implementation.

Pilot Testing
A pilot testing was done by taking small subset of  programs 
to verify the data collection and analysis procedure. 
This would allow strengthening the methodology, and 
it also guaranteed the effectiveness of  the tools used in 
extracting significant insights.

Ethical Issues
Since the data utilized in the study was available in the 
open there were no serious ethical concerns. Nevertheless, 
the research guaranteed the secrecy of  all sensitive data 
through the anonymization of  any personal data, as well 
as observance of  data use agreements established by 
government agencies. There were no direct contacts with 
the human subjects as the study was based on secondary 
sources of  data.

Measures and Variables
Operational Definitions
Dependent Variables
Environmental, where the dependent variable is measured 
by environmental impact (via metrics such as emissions 
reduction, energy efficiency improvements and waste 
reduction); economic, where the dependent variable 
is measured by cost savings, return on investment and 
job creation; and social where the dependent variable is 
measured by public engagement and policy compliance.

Independent Variables
Adoption and the span of  federal sustainability 
programmes and the deployment of  business intelligence 
tools to monitor and evaluate.

Measurement Tools
The information on the extent of  environmental and 
economic impact of  the programs was taken with 
publicly reported data, such as the statistics on emissions 
and financial performance indicators. The visualizations 
of  the trends and the creation of  predictive models based 

on past data were achieved by using business intelligence 
tools such as Tableau.

Reliability and Validity
Reliability was addressed by employing established 
measures of  data, i.e. government sustainability reports 
with high-quality standards of  data collection. This 
methodology was explained by using multiple sources as 
a source of  data and comparing the results with other 
related research in the sphere.

Data Analysis Plan
Analytical Techniques
The analysis of  the data was carried out by regressing 
the data with the relationships between the federal 
sustainability program implementation as an independent 
variable and the environment, economic and social 
impacts as the dependent variables. Simple statistics like 
the mean, median, and standard deviation was calculated 
to describe the data. Also, predictive analytics models 
were applied in order to help to predict future trends 
using the historical data.

Software
The statistical modeling and data exploration were performed 
with the help of  R and the visualization of  the data with 
Tableau. The R was chosen due to its strong statistical 
functions, whereas Tableau helped to construct interactive 
dashboards in order to identify trends and patterns.

Reasoning
The analysis techniques employed were selected due to 
the possibility to explore their data richly, reveal major 
tendencies, and elaborate forecasting models that will 
help make future endeavors more sustainable. The 
regression was especially employed in the testing of  
hypothesis concerning the association between program 
implementation and performance.

Ethical Considerations
The nature of  the study did not need human subjects and 
the article utilised publicly available data therefore there 
was no requirement to obtain formal ethical approvals. 
Nevertheless, the ethnical rules were adhered to that 
allowed correct data processing and reporting. Because 
no human participants are involved in the research, 
the process of  informed consent was inapplicable. To 
guarantee the privacy and confidentiality of  all the data, 
they were all anonymized. The study was based on 
publicly available data of  government agencies only.

Limitations
The use of  secondary data can be another source of  
bias because it might not be comprehensive and current 
at all times. Also, the availability of  long-term data 
regarding some of  the programs may limit the study. 
The research has a limitation due to the underlying 
coverage of  the available data that may not encompass 



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all federal sustainability programs, or consider all the 
particularities of  its impact. Moreover, some business 
intelligence process may lack the ability to pick up the 
unpredictable happenings of  sustainability activities 
by using historical data. These limitations could be a 
drawback in generalization of  the findings to all the 
federal sustainability programs in the U.S., although the 
study serves as an insightful approach in measuring the 
success of  sustained sustainability with a data-driven 
model. Such methodology will provide an intensive and 
transparent way of  examining the long term-effect of  
the U.S. federal sustainability programs. The proposed 
study will therefore use business intelligence tools and 
more powerful statistical methodologies to incorporate 
a practical understanding that could inform upcoming 
policy formulation and effectiveness of  sustainability 
programs in promotion.

RESULTS AND DISCUSSION
This paragraph shows the results of  the research of  
essential variables about the long-term effect of  U.S. 
federal sustainability programs by giving attention to 
income reduction, cost savings, energy efficiency, citizen 
involvement, program unemployment and business 
intelligence (BI) machine utilization and government 
investments. Table 1 describes the descriptive statistics of  
each variable.

Emissions Reduction
The reduction in the means of  emissions of  the sampled 
programs was 400,000 Tons of  CO2 (SD = 150,000). The 
midpoint decrease was a bit lower to 350,000 tons meaning 
that the programs favored to have a greater reduction 
in emissions. The spread in reductions of  emissions 
was quite large where a minimum of  100,000 and the 
maximum of  700,000 was achieved. This dispersion 
implies that even though a large number of  programs 
have reduced by a large percentage, some programs have 
been exceedingly successful and contributed a large part 
of  the total reduction.

Cost Savings
The costs saved differed substantially across the 
programs with the average being USD 4.5 million (SD = 
2,000,000). Median of  the cost savings of  USD 4 million 
suggests a tendency toward the center of  the distribution, 
but such range of  impact, minimum saving of  USD 1 
million and maximum of  USD 8 million, implies broad 
variations in the effectiveness of  alternative programs. 
These data show that although majority of  the programs 
were moderate in savings, few programs yielded high cost 
efficiencies.

Energy Efficiency
The mean improvement in energy efficiency was 14.3 
percent (SD = 5.0) and median 15 percent. Distribution 
of  energy efficiency gain was between 5% and 20% which 
indicated a quite wide dispersion in the effectiveness of  

the program in enhancing energy efficiency. These values 
imply that the majority of  the programs were shown to 
be adequately efficient in increasing energy efficiency 
within a moderate level but a fewer number of  programs 
could bring in a high level in terms of  efficiency.

Public Engagement
There was a huge difference among programs in the 
involvement of  the public with the mean number 
of  stakeholders engaged being 10,000 (SD = 4,500). 
The median is indicated as 9, 500, thus indicating a 
concentration set of  programs falling near to this figure 
in engagement levels. The outreach and involvement 
also differed with a minimum of  4,500 stakeholders to 
a maximum of  20,000 stakeholders as regards to the 
involvement of  the public. These results indicate that 
the reach of  the public participation was wide in certain 
programs, whereas others were on more limited, specific 
audiences.

Program Duration
The mean length of  the programs was 8.2 years (SD = 2.5), 
and the median one was 8 years. The shortest program 
went to a span of  5 years and the longest program was 
12 years. The data implies the relative stability of  the 
program length in the sample with some longer programs 
as it might indicate an ongoing or developing program.
The use of  BI Tools
The programs in this sample used business intelligence 
tools (mean = 0.6, SD = 0.5) to the approximate 
percentage of  60. The binary characteristic of  this variable 
(1 = Yes, 0 = No) reveals that 40 % of  the programs 
were not integrated with BI tools, whereas 60 % of  the 
programs accept the use of  BI tooling. This means that 
there is an increased dependence on data analytics as far 
as management of  sustainability programs is concerned, 
but there is still space to increase the use of  BI tools.

Government Funding
The mean average of  the amount that the government 
expended on the programs was USD 10, 000,000 (SD = 5, 
000, 000). The average funding looked a little less with the 
median figure being USD 8.5 million which means that 
there is a central tendency towards this type of  number. 
The minimum funding of  USD 2.5 million and maximum 
of  15 million US dollars showed a huge difference in the 
funding levels of  various initiatives. Such disparities are 
probably because of  differences in the sizes and industries 
and particular goals of  the initiatives. The descriptive 
statistics would give a description of  the performance 
and features of  federal sustainability programs. The 
broad spread of  numerous variables, such as reduction of  
emissions, saving cost, energy efficiency, and community 
involvement, points out the different effects and range 
of  these programs. Additional evaluation will be reflected 
in the connections of  these variables and how they are 
affecting long term results.



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Correlation Analysis
The correlations analysis investigated the associations 
that existed among the major variables linked to the 
U.S. federal sustainability programs where numerous 
significant relationships were found. The Pearson 
correlation matrix, Table 2 shows the degree and direction 
of  the relationship between the variables.
There was a high positive relationship found between 
the reduction of  emissions and cost savings (r = 0.88, 
p < 0.01) given that programs with higher reductions 
concerning emissions also showed higher cost savings. 
Likewise, the reduction of  emissions was positively 
related to the energy efficiency (r = 0.80, p < 0.01), which 
implied that initiatives that were created to reduce the 
number of  emissions provided also positive outcomes 
to the energy efficiency. The correlation between the 
reduction of  emissions and the participation of  people 
(r = 0.70, p < 0.01) was moderate and even significant, 
which means that more people engaged programs led to 
the higher reduction of  emissions. Moreover, the cost 
saving was strongly correlated with energy efficiency (r 
= 0.75, p < 0.01), which shows that programs associated 
with more cost saving were also inclined towards an 
increase in energy efficiency. The cost savings further 
showed to be highly positively correlated with the 
engagement of  the population (r = 0.66, p < 0.01) and the 
use of  business intelligence tools (r = 0.74, p < 0.01) and 
so the more the population was engaged and the more 
business intelligence tools were employed, the more cost 

savings also occurred. Also, the topic of  cost savings was 
found to have a high correlation (r = 0.80, p < 0.01) with 
government funding indicating that the levels of  financing 
were higher with more financial savings on the programs. 
Concerning energy efficiency, a moderate correlation 
was established with the levels of  engagements with the 
public (r = 0.58, p < 0.01), meaning that the greater the 
level of  engagement with the population, the greater the 
energy efficiency. The positive correlation was also very 
strong between energy efficiency and use of  BI tools (r 
= 0.65, p < 0.01) indicating that employment of  business 
intelligence tools facilitated optimisation of  energy 
efficiency improvement.
The correlation between the government-funding 
expenditure and the utilization of  BI tools was also 
significant (r = 0.68, p < 0.01), which means that 
programs having more government-funding expenditure 
had higher likelihood of  employing business intelligence 
tools as part of  its procedures. Lastly, there was a 
moderate correlation between public engagement 
and government funding (r = 0.65, p < 0.01), which 
demonstrates that the more the programs have reached 
out to the population, the more government funding they 
have attracted. All in all, the discussed correlations prove 
that emissions reduction, cost saving, energy efficiency, 
and involvement of  people are too tightly intertwined. 
Further, the business intelligence tool implementation 
and government funding were the variables that showed 
positive results in all of  the significant variables equally.

Table 1: Descriptive Statistics of  Key Variables
Variable Mean Median Standard Deviation Minimum Maximum
Emissions Reduction (tons CO2) 400,000 350,000 150,000 100,000 700,000
Cost Savings (USD) 4,500,000 4,000,000 2,000,000 1,000,000 8,000,000
Energy Efficiency (%) 14.3 15 5.0 5 20
Public Engagement (# of  stakeholders) 10,000 9,500 4,500 4,500 20,000
Program Duration (Years) 8.2 8 2.5 5 12
BI Tool Usage (1 = Yes, 0 = No) 0.6 1 0.5 0 1
Government Funding (USD) 10,000,000 8,500,000 5,000,000 2,500,000 15,000,000

Table 2: Pearson Correlation Matrix
Variable Emissions 

Reduction
Cost 
Savings

Energy 
Efficiency

Public 
Engagement

BI Tools 
Used

Funding

Emissions Reduction 1.00 0.88** 0.80** 0.70** 0.72** 0.76**
Cost Savings 0.88** 1.00 0.75** 0.66** 0.74** 0.80**
Energy Efficiency 0.80** 0.75** 1.00 0.58** 0.65** 0.72**
Public Engagement 0.70** 0.66** 0.58** 1.00 0.60** 0.65**
BI Tools Used 0.72** 0.74** 0.65** 0.60** 1.00 0.68**
Government Funding 0.76** 0.80** 0.72** 0.65** 0.68** 1.00

* p < 0.05, p < 0.01

Multiple Regression Analysis
The analysis was done by multiple regression in order 
to assess the effects of  some independent variables 

on the dependent variable of  reduction of  emissions 
which include the variable on government funding, 
duration of  the program and use of  BI tools. The 



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findings of  a regression analysis can be seen in Table 
3. As indicated on the regression model, government 
funding (beta = 0,00002, p = 0.001), duration of  the 
program (beta = 15000, p = 0.007) and the usage of  
BI tools (beta = 120000, p = 0.005) were all statistically 
significant predictors of  the reduction of  emissions. The 
government funding coefficient means that as funding of  
1 more dollar was added, the emissions reduction enabled 
by it rose by 0.00002 tons of  CO2 and this is a strong 
positive effect. The duration of  a program demonstrated 
that with every one-year increase in the implementation 
of  a program, a proportionate reduction of  emissions at 
CO2 by 15,000 tons was improved and highlighted the 
benefit of  a long-term sustainability program further. BI 
tools also significantly contributed to the effect since it 
was observed that programs that included the use of  BI 
tools led to 120,000 additional tons in the reduction of  

CO2 compared to those that did not. The model had a 
variance explanation value of  85 percent (R 2 = 0.85) 
which is strong showing that the model is well developed. 
With all significant p-values (p < 0.01), the values of  the 
coefficients included in the model would be considered 
trustworthy, and the explanatory effect of  the factors in 
the independent variable would be substantial in regards 
to explaining the variability of  the emissions reduction. 
The regression analysis proved that the government 
funding, program time span and the use of  BI tools all 
played important roles in causing emissions reductions. 
Government funding had the greatest impact among 
the three, whereas the use of  BI tools and length of  
the program had an impact to a lesser degree. Through 
this analysis, one can appreciate the relevance of  both 
financial and technological support in increasing the 
environmental performance of  sustainability programs.

Table 3: Multiple Regression Results for Emissions Reduction
Variable Coefficient (β) Standard Error t-statistic p-value
Intercept 100,000 50,000 2.00 0.05
Government Funding (USD) 0.00002 0.000005 4.00 0.001
Program Duration (Years) 15,000 5,000 3.00 0.007
BI Tool Usage (1 = Yes, 0 = No) 120,000 40,000 3.00 0.005

The relationships between the various independent 
variables, government funding, program duration and BI 
tool usage, and the one dependent variable of  emissions 
reduction were analyzed using a multiple regression 
analysis. Table 3 shows the regression analysis. The 
regression coefficient model indicated the significant 
predictors of  reduction of  emissions were government 
funding (0.00002, p = 0.001), and the duration of  the 
program (15,000, p = 0.007), as well as the BI tools 
use (120,000, p = 0.005). The government funding 
coefficient shows that every extra dollar used in the 
government funding, it led to 0.00002 tons of  CO2 
emissions reduction, which had a very positive impact 
as shown. Program length demonstrated that with 
every extra year of  implementation of  the program, the 
amount of  emissions reduction grew by 15,000 tons 
of  CO2, which once again affirms the positive effect 
of  sustainability programs that last many years. BI tool 

usage was another influential factor since programs 
utilising BI tools used led to 120,000 additional tons of  
the reduction of  CO2 in comparison to programs utilising 
no BI tools. The R 2 value of  0.85 (adjusted R 2 is 0.83) 
demonstrates a close match to the model that explains 
85 per cent of  variation in the reduction of  emissions. 
The high p-values of  the values of  all the predictors (p 
< 0.01) indicates that the model coefficients are accurate 
and that the independent variables play a great role in the 
explanation of  variability in the reduction of  emissions. 
The regression analysis showed that all outlets played 
a role in encouraging emissions reductions, including 
the amount of  government financing, duration and 
the use of  BI tools. The best response was seen in the 
area government funding, then the BI tools and the 
duration of  the program. In this study, the use of  money 
and technology has been highlighted in promoting the 
environmental success of  sustainability initiatives.

Table 4: Independent T-test Comparing Programs with and without BI Tools
Group Mean Emissions 

Reduction (tons CO2)
Mean Cost Savings 
(USD)

t-statistic p-value

BI Tools Used (1) 500,000 5,500,000 3.21 0.002
BI Tools Not Used (0) 300,000 3,000,000 2.89 0.005

One-Way ANOVA 
Comparisons of  the mean emissions reduction between 
the programs of  a different lengths (5 years, 10 years, and 
12 years), a One-Way ANOVA was performed. Table 5 
indicates the results. Five-year programs had an average 
emission reduction of  300,000 tons of  CO2 whereas 

marginal emissions reductions of  programs with 10 years 
and 12 years durations were found to be 500,000 tons 
and 650,000 tons, respectively. The F-statistic of  this 
comparison was 4.25, and the p-value stood at 0.023, and 
it denoted the fact that the difference in the reduction of  
emissions based on the three groups is significant at the 



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0.05 level. Post-hoc test showed that the 10 and 12 year 
programs resulted in significant reduction of  emissions as 
compared to the 5 year programs. In particular, measures 
of  sustainability programs in terms of  emissions 
reduction were higher in longer program durations (10 
and 12 years), indicating that the quality of  sustainability 
programs when the goal is to reduce emissions is better 

in the programs with longer implementation period. 
Overall, ANOVA results show that there is significance 
between emissions reduction based on the program 
length. The program of  longer duration of  10 and 12 
years was more effective in registering higher reductions 
levels as compared to a program of  short duration i.e. 5 
years.

Table 5: One-Way ANOVA Comparing Emissions Reduction by Program Duration
Duration (Years) Mean Emissions Reduction (tons CO2) F-statistic p-value p-value
5 years 300,000 4.25 0.023 0.002
10 years 500,000 0.005
12 years 650,000

Chi-Square Test
A Chi-Square test was performed to assess whether there 
was a significant association between BI tool usage (1 = 
Yes, 0 = No) and program success, defined as achieving 
cost savings greater than $5 million. The results of  the 
Chi-Square test are summarized in Table 6.
The programs that used BI tools, 8 out of  12 (66.7%) 
achieved cost savings greater than $5 million, while only 
2 out of  8 (25%) of  the programs that did not use BI 
tools met this threshold. The Chi-Square statistic was χ² 

= 5.88, with a p-value of  0.015, indicating a statistically 
significant association between the two variables at the 
0.05 significance level. These results suggest that the 
use of  BI tools is significantly associated with program 
success, as programs that utilized BI tools were more 
likely to achieve cost savings above $5 million compared 
to those that did not. The p-value of  0.015 confirms that 
this association is statistically significant, implying that 
BI tool usage contributes positively to the likelihood of  
higher program success in terms of  cost savings.

Table 6: Chi-Square Test for BI Tool Usage and Program Success
BI Tool Usage (1 = Yes, 0 = No) Program Success (Cost Savings > $5 million) Total
Yes (1) 8 12
No (0) 2 8
Total 10 20

Figure 1: Descriptive statistics of  key variables Figure 2: Regression cofficients for emissions reduction



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Figure 3: Emissions reduction by BI tool usage and Cost saving by BI tool usage

Figure 6: Pearson correlation matrix

Figure 4: Emission reduction by program duration 
(ANOVA)

Figure 5: Program success bu BI tool usage



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Discussion
The purpose of  this study was to determine the long 
term effects of  the sustainability programs the federal 
government of  the U.S has established, and further 
more the researcher aims at determining how business 
intelligence (BI) tools could help to determine the 
effectiveness of  these sustainability programs. Through 
running statistical evolutionary methods (e.g., multiple 
regression analysis, T-tests, ANOVA, Chi-Square tests), 
we have discovered meaningful results concerning the 
connection between program features (e.g., BI tool 
application, trimester of  the program, government 
funding) and diverse sustainability outcomes, including 
reductions in emissions and cost savings (Okaily et 
al., 2023). The findings show that the application of  
the BI tools, programming period, and the amount of  
government funding are of  extreme importance to the 
success of  sustainability programs, especially regarding 
the reduction of  emissions and cost efficiency.
The result of  the point in this research paper is that 
the usage of  BI tools, the length of  program, and 
governmental investment played a significant role in the 
success of  sustainability programs in attainment of  not 
only emissions reduction but the cost savings as well. As 
we have statistically found in the regression model, the 
most important influencing factor in the reduction of  
emissions was government funding, then the length of  
a program, and finally the application of  BI tools. This 
major association between the use of  the BI tools and 
enhancement of  sustainability results is consistent with 
the developed impulse towards the application of  data-
driven decision-making to the world of  environmental 
policy (Ncube & Ngulube, 2024). Positive association 
between the use of  BI tool and cost savings further 
extend the fact that the use of  technology is very crucial 
in triggering effective management of  resources and cost 
effective environmental policies. These results indicate 
that, overall, federal sustainability projects could be 
significantly more successful in the long-term perspective 
both in environmental and economic terms, provided a 
modern data analytics approach is incorporated into the 
program (Latupeirissa et al., 2024).
In addition, the results of  the One-Way ANOVA 
indicated that the emissions reduction of  the longer 
programs (10 and 12 years) was considerably large than 
that of  shorter programs (5 years). This observation 
proves the hypothesis that sustainability programs need 
time to be fully beneficial to the environment (Coffield 
et al., 2022). It is expected that program duration may 
result in more extensive implementation of  strategies, 
adaptation to changing technologies, and thorough 
incorporation of  the sustainability practices in different 
sectors. These findings support the previous evidence 
provided by (Stern, 2022) and Khalil et al. (2024), who 
stated that the efficacy of  sustainability programs grows 
over time, as such schemes gain experience and advance 
their procedures and enjoy economies of  scale.
Moreover, the findings of  the Chi-Square test showed that 

there is a significant relationship between the application 
of  the BI tools and the success of  the program especially 
relating to cost saving more than 5 million dollars. Projects 
that used BI tools had more chances to gather large 
cost savings. This observation is significant in terms of  
showing, as policymakers and program managers will use 
it to monitor performance, achieve resource allocation 
efficiency, and make effective decisions. The potential to 
use BI tools in the sustainability plan, therefore, would 
boost a more sound approach to financial management 
and environment-based strategies and ease its recent 
negative influence on the performance of  the public 
sector.
Similar results about the beneficial effects of  BI tools 
on program success have also been observed by Alonge 
et al. (2023) and Kommineni et al. (2024) who explained 
the usefulness of  business intelligence in improving 
the decision-making processes accessed in the energy 
and environmental context. These analyses showed that 
data analytics tools, in addition to enhancing the process 
of  monitoring sustainability outcomes, result in trend 
identification, forecasting of  future results, and resource 
optimization (Ncube & Ngulube, 2024). Likewise, data-
driven methods have been demonstrated to have positive 
results in the Green New Deal and Energy Independence 
and Security Act of  2007 although the effectiveness of  
the programs is barely studied with the level of  rigor as 
the one in this research (Bibri & Krogstie, 2021).
Moreover, the conclusion that we reached that with 
lengthier program periods, there is a stronger correlation 
with emissions reductions fits in line with the study that 
researchers Soomro and others(2024) have come to find 
that sustainability initiatives will be more pronounced in 
their outcomes over extended periods of  time. It accords 
with biological and physical principles of  environmental 
change, whereby as the effects of  interventions slow 
to a crawl, as their weighty accumulation ensues, their 
impacts upon environments, energy systems, and climatic 
conditions are consequently magnified. The regression 
analysis further found out that government investment 
positively and indeed, significantly influences reduction 
in emissions. The same idea can be traced in the findings 
of  (Mahmood et al., 2024; Oshilalu, 2024), who observed 
that financial investment in sustainability programs could 
be an influential factor of  success as this investment 
ensures the integration of  advanced technology, research, 
and infrastructure enhancement required to make 
environmental goals successful in the long term.
The positive correlation between government funding and 
emission reduction can be attributed to the fact that such 
funds imply better resources allocated to such programs 
whereby one can scale interventions, switch to cleaner 
technology and fund research and development (Pandey 
et al., 2022). Specifically, the funding will allow investing 
in renewable energy technologies and energy-efficient 
infrastructure along with carbon capture systems, which 
are the key interventions to mitigate the greenhouse 
gas emissions. The budget is also vital in ensuring that 



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the sustainability programs are effectively tracked and 
managed hence further improving its results (Arshad & 
Parveen, 2024). The reported positive effects of  longer 
durations in reducing emissions can be according to the 
cumulative impact which is an effect of  sustainability 
measures. With gradual gains on environmental 
regulation like the reduction of  emissions, a lot of  time 
and continual input is needed to have a substantial effect 
(Ekins & Zenghelis, 2021). Programs also improve in 
the long run, having acquired additional data, optimized 
their strategies, and with the advantages of  technological 
development, resulting in better outcomes. Particularly, 
this happens in the energy and transportation sectors, 
where investments in infrastructure and innovation are 
long-term shifts and have to be made to influence the 
level of  emissions significantly in the new extreme (Khan 
et al., 2022).
The results obtained on the effects of  BI tools on the 
cost-saving might be attributed to the efficiency that 
advanced data analytics enables. With BI tools, the 
program managers will have access to real-time data 
on the performance of  different sustainability projects, 
including its inefficiencies, and streamlining the utilization 
of  resources (Chalmeta & Ferrer, 2023). Such tools also 
lead to predictive analytics, where the decision-makers are 
capable of  knowing what will happen in the future and 
acting proactively to those challenges. In such a manner, 
BI tools are useful to reduce expenses, optimize resource 
utilization, and enhance the efficiency of  sustainability 
initiatives in general.
The results of  the current research can be significant 
to future research, policy and practice in the study of  
sustainability. To begin with, the beneficial effect of  the 
BI tools on the reduction of  both emissions and costs 
emphasize the necessity of  incorporating contemporary 
data analytics in sustainability (Ojadi et al., 2024). The 
topic of  future research should also extend into the 
discussion of  particular BI tool features that may lead to 
their success, like data visualization, predictive modeling, 
and real-time monitoring. Moreover, policymakers can 
also look into the integration of  BI technologies in 
the development of  sustainability programs in terms 
of  evaluations and efficiency to maximize long-term 
outcomes and thriving success.
The findings also suggest that there is the necessity of  
longer engagements toward sustainability. Considering 
the results stating that program duration was one of  
the key factors in attaining greater emissions decreases, 
policymakers need to put a heavy focus on the long-run 
efforts (Nemet et al., 2023). The short-term projects can 
provide few opportunities in changes whilst the long-
term projects can give more promises of  changes taking 
place on the environment. Lastly, the paper identifies the 
need to ensure proper funding in boosting the success of  
sustainability programs. The governments need to make 
sure that the required amount of  monetary finances can be 
directed to fund the sustainable growth of  sustainability 
initiatives and the integration of  the latest technologies 

(Shan & Ji, 2024). It is especially applicable given that 
countries are striving to achieve ambitious climate goals 
and handle the threats of  climate change.

Limitations
As useful as the study is in highlighting long-term 
effects of  sustainability programs of  the U.S. federal 
government, it is necessary to mention some limitations. 
The researchers utilized the secondary data which was 
publicly available; the subjects of  the study might have 
missed some of  the relevant variables or specifics of  the 
program performance. Besides, the research looked at 
only the federal sustainability programs, which implies 
that the results cannot be directly applied to the state or 
local projects. These limitations could be improved in 
future research using a more granular primary data and 
a wider sample of  sustainability programs of  various 
levels of  government. Additionally, the research lacked 
the mechanisms of  potential interactions among the 
variables (e.g., how both government funding and the use 
of  BI tools might impact the reduction of  emissions), 
which would help to have a more in-depth picture of  the 
factors contributing to the results of  sustainability. It may 
be possible to use higher methodological modeling, like 
structural equation modeling in future studies to study 
these interactions.

CONCLUSION
The paper has used business intelligence (BI) tools 
to analyze the long-term influences of  U.S. federal 
sustainability programs with environmental, economic, 
and social consequences being the measures of  
the effort. After the results, there was a significant 
correlation between the use of  BI tools and the reduction 
of  emissions, and cost savings. The programs which 
incorporated BI tools were more effective when it comes 
to reducing the emissions and ultimately increasing the 
cost savings than the ones which did not incorporate the 
relevant BI tools. Also, extended program duration had 
correlation with bigger reduction of  emissions inferring 
the usefulness of  more prolonged sustainability measures. 
The Chi-Square test emphasized strongly on the fact that 
there was a significant correlation between the BI tools 
and greater program success in terms of  cost savings.
This study has achieved its goals, showing the 
applicability of  BI tools in assessing and enhancing 
the performance of  federal sustainability programs.
BI tools assess sustainability programs by tracking and 
analyzing performance data, and enhance them by 
providing evidence-based insights for improvement.
BI tools assess sustainability programs by tracking and 
analyzing performance data, and enhance them by 
providing evidence-based insights for improvement. It is 
scientifically useful because it reports data-drawn answers 
as to how BI can assist in the environmental policy 
analysis and the effectiveness of  programs. In general, the 
work can serve as a base of  further investigations of  the 
topic concerning the incorporation of  advanced analytic 



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in sustainability programs. The study may be extended in 
future, based on program level data that is more detailed 
or one may also extend it and cover the international 
sustainability programs.

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