The Differential Impacts of Human Capital and Infrastructure on the Sustainable Development Goals

June 5, 2023May 26, 2025 hpanahovLeave a comment

ABSTRACT:

This study looks at country-level data to explore the dynamics among human capital, infrastructure, and a country’s progress toward the United Nations Sustainable Development Goals (SDGs). Utilizing the confirmatory factor analysis method, I develop a new Infrastructure Index and combine it with the World Bank’s dataset on Human Capital Index to evaluate the relative impact of these factors on a country’s SDG scores. My findings affirm the integral roles of both human capital and infrastructure in the sustainable development context. However, a stronger correlation between human capital and the SDG Index suggests that policymakers seeking to advance the sustainability agenda should prioritize investments in human capital over infrastructure. Moreover, the study uncovers nuanced relationships between these indicators and specific SDGs. Human capital has a significant association with SDG 5 (Gender Equality), whereas infrastructure does not. Both human capital and infrastructure affect SDG 1 (No Poverty), with no statistical difference between their effects. Interestingly, while human capital correlates more strongly with SDG 13 (Climate Action), this relationship is negative due to the larger carbon footprint of more developed economies. These findings can inform policy decisions for goal-specific sustainable development strategies.

I. INTRODUCTION:

The central framework in the global development agenda is based on the 2030 Agenda for Sustainable Development, which “provides a shared blueprint for peace and prosperity for people and the planet, now and into the future.” It is undersigned by all UN Member States. Hundred-ninety-one countries have committed to achieving measurable progress on these goals by 2030. The Agenda constitutes seventeen interlinked Sustainable Development Goals (SDGs) that encompass a very wide variety of objectives. The seventeen SDGs are broken down into hundred-sixty-nine targets and two-hundred-thirty-two indicators to measure progress.

Measuring progress

One of the challenges in the SDG framework is measuring the progress in order to inform the policy. SDGs are successors to the Millennium Development Goals (MDGs), which consisted of 8 goals and 18 targets, 14 of which could be assessed quantitatively. MDGs were adopted in 2000, and all the countries from around the world committed to achieving these goals within 15 years. By the end of 2015, only three and a half of the 14 measurable targets were achieved. In 2023, we are at the half-way mark of the 2030 Agenda. According to the latest reports, the international community is behind schedule to achieving the SDG’s, partially due to the impact of the COVID-19.[1] In the given context, one of the most important questions is to find what policy interventions would be most effective to advance progress towards the SDGs.

What interventions are most effective?

Investments in both human capital and infrastructure are critical for achieving the sustainable development goals. These are both interdependent and complimentary domains in the international development space. However, policymakers working on specific developmental objectives are often forced to prioritize one over the other due to the limited nature of resources. This research analyzes country-level data from the United Nations and the World Bank to estimate the relationship between the overall SDG Index of a country and its performance on the Human Capital Index and Infrastructure Index. I will also examine the impact of human capital and infrastructure on SDG 1 (No Poverty), SDG 5 (Gender Equality), and SDG 13 (Climate Action). Below I provide more information about each one of the concepts analyzed in this research.

SDG Index

SDG Index is a composite indicator developed by the United Nations that weighs in the effects of development metrics across all the SDG metrics. It estimates countries’ performance on a scale from 0 to 100, and usually, Scandinavian countries, such as Finland, Denmark, Sweden, and Norway, achieve the highest rankings with scores > 80.[2] The 2022 Report includes the SDG indexes for 163 countries, among which the Central African Republic and South Sudan have the lowest scores, sub-40.

SDG 1: No Poverty

The first goal in the UN SDG framework calls to “end poverty in all its forms everywhere.” SDG 1 aims to ensure that everyone, regardless of their circumstances, has equal access to opportunities and resources for a quality life. It calls for comprehensive strategies to end poverty that include social protection systems and measures to build the resilience of the poor and those in vulnerable situations. The three main metrics of SDG 1 are: poverty headcount ratio at $1.90/day (%), poverty headcount ratio at $3.20/day (%), and poverty rate after taxes and transfers (%).

SDG 5: Gender Equality

Gender equality is fundamentally important for achieving the Sustainable Development Goals for several reasons. First, it is a matter of human rights. Everyone, regardless of gender, should have equal access to health, education, economic opportunities, and political representation. Second, gender equality is pivotal for economic growth, as women constitute half of the world’s potential human capital, and studies consistently show that societies that discriminate by gender tend to experience less economic growth and slower poverty reduction. The SDG 5: Achieve Gender Equality and Empower all Women and Girls incorporates the following metrics: the ratio of female-to-male mean years of education received (%), the ratio of female-to-male labor force participation rate (%), seats held by women in national parliament (%), gender wage gap (% of male median wage).[3]

SDG 13: Climate Action

SDG 13 calls for immediate action to combat climate change and its impacts. The Goal underscores the critical need for the global community to address the pressing issue of climate change. Recognizing that climate change is not just an environmental issue but also a significant threat to social and economic development, this goal calls for urgent action to reduce greenhouse gas emissions, build resilience, and improve adaptive capacity to climate-induced impacts. The metrics of SDG 13 include CO₂ emissions from fossil fuel combustion and cement production (tCO2/capita), CO₂ emissions embodied in imports (tCO₂/capita), CO₂ emissions embodied in fossil fuel exports (kg/capita), Carbon Pricing Score at EUR60/tCO₂ (%, worst 0-100 best).[4]

Statistical Performance Index

The Statistical Performance Index (SPI) evaluates the performance of national statistical systems based on the aggregate of five pillars of statistical capacity: data use, data services, data products, data sources, and data infrastructure. The SPI is a weighted average of the statistical performance indicators.

Human Capital Index

Human capital is sometimes referred to as soft infrastructure.[5] Without thriving human capital, nations cannot achieve their development goals, highlighting its central role in international development. It is widely acknowledged that improvements in human capital lead to increased productivity, which in turn spurs economic growth. Education and health, the two main components of human capital, have a direct impact on a country’s development trajectory. In 2018, the World Bank developed the Human Capital Index as a metric to measure and evaluate the quality and potential of human capital in a country. The HCI enables policymakers to identify strengths, weaknesses, and areas for improvement in human capital development. The HCI is based primarily on three components:

Child survival: This component considers that not all children survive to start formal education and looks at the under-5 mortality rate.
Education: This section combines information on the quality and quantity of education. The number of years a child is expected to complete school by age 18, considering current enrollment rates, measures the quantity of education. The quality is assessed using harmonized test scores from international student achievement testing programs.
Public health: This component uses two proxies for the overall health environment – adult survival rates (the percentage of 15-year-olds who will survive until age 60) and healthy growth among children under 5, measured by stunting rates.[6]

Infrastructure Index

According to the Merriam-Webster dictionary: Infra- means “below,” so the infrastructure is the “underlying structure” of a country and its economy, the fixed installations that it needs in order to function.”^[7] Public infrastructure provides the basic physical systems and structures, such as water supply, sewers, electrical grids, roads, bridges, and telecommunications, among others. High-quality infrastructure ensures the provision of fundamental necessities, advances safety, and enhances the quality of life. Infrastructure also facilitates the exchange of reliable information, increases productivity, creates more job opportunities, and fosters overall economic growth.

Unlike the Human Capital Index, there is no internationally recognized index that would indicate the level of public infrastructure in a given country. The objective of the UN SDG 9 is to “Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.”[8] However, for the purposes of this research, it is not the best pointer because it includes indicators, such as Expenditure on Research and Development, Female share of graduates from Science, Technology, Engineering, and Mathematics (STEM) programs, but does not include indicators for access to electricity, water supplies, etc. However, there are seven SDG indicators across four different sustainable development goals that are related directly to the public infrastructure:

Indicator	Description	SDG
1. Access to basic water services	The percentage of the population using at least a basic drinking water service, such as drinking water from an improved source, provided that the collection time is not more than 30 minutes for a round trip, including queuing.	SDG 6: Ensure availability and sustainable management of water and sanitation for all
2. Access to basic sanitation services	The percentage of the population using at least a basic sanitation service, such as an improved sanitation facility that is not shared with other households.
3. Access to electricity	The percentage of the population who has access to electricity.	SDG 7: Ensure access to affordable, reliable, sustainable and modern energy for all
4. Adult population with bank accounts	The percentage of adults, 15 years and older, who report having an account (by themselves or with someone else) at a bank or another type of financial institution, or who have personally used a mobile money service within the past 12 months.	SDG 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
5. Internet penetration	The percentage of the population who used the Internet from any location in the last three months. Access could be via a fixed or mobile network.	SDG 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
6. Transportation systems	The percentage of the surveyed population that responded “satisfied” to the question “In the city or area where you live, are you satisfied or dissatisfied with the public transportation systems?”.	SDG 11: Make cities and human settlements inclusive, safe, resilient and sustainable

Hypotheses:

The question driving this research is to find the differences in the effects of human capital and infrastructure on SDG scores. So, I have constructed the following hypotheses:

H₀:	There is no statistical difference in the effects of Human Capital and Infrastructure on SDG Index
H₁:	There is a statistical difference in the effects of Human Capital and Infrastructure on SDG Index
H₂:	There is a statistical difference in the effects of Human Capital and Infrastructure on SDG 1: No Poverty
H₃:	There is a statistical difference in the effects of Human Capital and Infrastructure on SDG 5: Gender Equality
H₄:	There is a statistical difference in the effects of Human Capital and Infrastructure on SDG 13: Climate Action

II. METHODS

Merging the data sets

I merge the World Bank Human Capital Index and the UN Sustainable Development 2022 datasets with the Country name as the unique identifier. When I drop the rows with missing HCI Index or the SDG Index values, the number of entries in my data frame reduces from 201 to 141. Part of the reason is that UN SDG data also includes geographic Regions (such as “East and South Asia” or “Latin America and the Caribbean”) and Income categories (such as “Low-income Countries” or “Upper-middle-income Countries”) under the Country variable. With that being said, there are also missing values in both data sets. Nonetheless, we still have 141 complete data rows, which is sufficient for us to proceed with our analysis.

Factor Analysis

Public infrastructure is a broad concept which we cannot easily observe and measure. In statistical terms, it is a latent variable, which refers to “concepts that cannot be measured directly but can be assumed to relate to a number of measurable manifest variables.”[9] I use the factor analysis technique, which allows me to account for various dimensions of the public infrastructure (such as water, electricity, internet, etc.) and output one variable. Factor Analysis is often used for constructing a new index, as it explores and uncovers the underlying relationships between observed manifest variables and unobserved latent variables.

KMO Test

The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is a statistic that indicates the proportion of variance in the variables. The KMO values range from 0 to 1, with higher values indicating a better fit for factor analysis. The individual KMO values for each variable tell us how well each variable fits with all the others. Variables with a KMO less than 0.5 might not be suited for factor analysis as they do not correlate well with the other variables. As we see from the below output, the MSA values of all my variables are 0.8 or above, which brings the overall MSA score to 0.87, which is a positive sign.

Kaiser-Meyer-Olkin (KMO) Test results

Model 1

So, I keep all six manifest variables to construct a model that will estimate the infrastructure index. In the first model, the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) are both above the 0.95 threshold, which indicates an appropriate fit. However, the Root Mean Square Error of Approximation is 0.099, above the maximum threshold of 0.08.

Infrastructure Index Model 1: fit estimates

Changing model specifications

Since the fit of the first model is not satisfactory, I change the specifications of the model based on the modification indices and theoretical considerations. Modification indices are a measure of how much the overall model chi-square would be expected to decrease if a particular parameter were freely estimated in the model. In other words, it provides a suggestion on how to improve the model fit. If we look at the modification indices between our variables, we notice that the relationship between sdg8_bank and sdg9_internet is disproportionately stronger than any other in our data. Most likely due to a strong correlation between the indicator for internet penetration and the percentage of the adult population who have bank accounts.

Modification Index table (descending order)

Model 2

So, I add a special path to my model, which accounts for the dependency between ‘SDG 8 bank accounts’ and ‘SDG 9 Internet’. I also create a path between ‘SDG 6 Water’ and ‘SDG 6 Sanitation’ because academic literature dictates that there is usually a strong dependency between these two variables. When I check the fit indices, the new model with two special paths performs much better than the previous one. Both the CFI and TLI values are > 0.99, and the RMSEA has decreased to 0.056.

Infrastructure Index Model 2: fit estimates

The Infrastructure Index

I use the model to estimate the infrastructure index for all 141 countries in our dataset. I also use the min-max normalization technique to transform the index into a new scale from 0 to 1. This method scales the values by subtracting the minimum value and dividing by the range of the original values (i.e., the difference between the maximum and minimum values). So, they maintain the same variance and proportions but on a scale from 0 to 1.

Estimating the Impact on the SDG Index

Both the Human Capital Index and the Infrastructure Index are ratio-level measures. When both independent variables are ratio level measures, “regression and correlation analysis are the standard techniques for measuring relationships and testing hypotheses.”[10] My main hypothesis is to test whether human capital or infrastructure makes a bigger impact on the overall SDG Score of a country. So, I construct a multivariate regression model with HCI and the Infrastructure Index as independent variables and then explore the beta coefficients of the model to understand which index has a stronger effect on the SDG Score.

III. RESULTS

Summary of the Multivariate Regression Model

Multivariate Regression Model

Besides the Human Capital Index and Infrastructure Index, I also have the Statistical Performance Index as an explanatory variable in my model. As mentioned earlier, it helps to account for some of the possible shortcomings in the data. We can see that all three variables and the model have a very high level of statistical significance, with p = 0. The R-squared value is not very important for us because we are looking at a descriptive model versus a predictive model. However, in any case, the multiple R-squared value is 0.91, which means that approximately 91% of the variability in the outcome variable can be explained by the predictor variables.

Model assessment: regression diagnostics

1. Test for Linearity

Before I proceed further with my analysis of the findings, I need to test the assumptions to validate that a linear regression model was a suitable approach. First, I look for linearity and equal variance in the below Residuals vs Fitted plot. Upon visual examination, there are no substantial deviations in the red line, which confirms that the relationship is linear between our explanatory and response variables.

2. Test for homoscedasticity

In the below plot, we can also observe that the vertical spread of the residuals is equally distributed, which means the error term does not vary much as values of the outcome variable change. So, our model passes the test for homoscedasticity as well.

3. Testing for Independence of residuals

Based on the observations from the below “Residuals vs Leverage” plot, our model passes the test of independence of residuals as well. Large residual values on this plot would suggest that the model is not explaining some aspects of the data. Our model does not have any standardized residual values above 1. In R programming language, I double-checked and confirmed no observations with Cook’s distance value above 1.

4. Testing for Normality of the error distribution

We can tell whether the error terms are normally distributed based on the observations from the below Q-Q plot. We want the residuals to be as close to the diagonal line as possible. However, generally, we rarely have real data where errors are perfectly normally distributed. So, some deviations are expected, and overall, it seems like our model passes the normality test. However, to double-check, I also apply the Shapiro-Wilk test.

The null hypothesis for the Shapiro-Wilk test is that the data is normally distributed. In this case, the p-value for the Shapiro-Wilk test is way above the significance level, which means that we cannot reject the null hypothesis, and the data is normally distributed.

5. VIF Score

Last but not least, since we are dealing with a multiple linear regression model, we need to make sure there is no multicollinearity. So, we apply the VIF Score test. “A rough rule of thumb is that variance inflation factors [VIF] greater than 10 give some cause for concern.” (Vehklahti p.93) As we can see from the below table, the VIF scores for all three of our independent variables are below 5. These scores indicate some multicollinearity but are safely within an acceptable range.

VIF Scores:

Beta coefficient analysis

After we have confirmed that model meets all 5 assumptions of a multivariate regression model, we can proceed with the analysis of the model. In order to estimate the impact of each individual variable on the SDG Index, we can look at the beta coefficients. The standardized beta coefficients allow us to compare the effects of the variables on the same scale, regardless of the units of measurement. Below are the beta coefficients of our linear multivariate regression model. We notice that the beta coefficient for hci_ind (Human Capital) is larger than the coefficient for infr_ind (Infrastructure). This suggests that Human Capital has a stronger impact on the output variable, the SDG Index.

Beta coefficients of the Multivariate Regression model

However, we also need to make sure the difference between the two beta coefficients is statistically significant. I run the below linear hypothesis test, which is based on the null hypothesis that there is no difference between the effects of the two indices: hci_ind and infr_ind.

Linear hypothesis test

The associated p-value (Pr(>F) = 0.0001545) is far below 0.05, indicating strong evidence to reject the null hypothesis that the coefficients for hci_ind (Human Capital Index) and infr_ind (Infrastructure Index) are the same. So, the data provides strong evidence that the effect of Human Capital on the sdg_ind (SDG Index) is different from the effect of Infrastructure (infr_ind) on sdg_ind.

Next, I explore the relationship between Human Capital Index, Infrastructure Index and specific Sustainable Development Goals: SDG 1: No Poverty; SDG 5: Gender Equality; and SDG 13: Climate Action. I construct a multivariate multiple regression model with three left-hand variables, indicators for SDG 1, SDG 5, and SDG 13.

Response SDG 1:

Based on the initial observation of the model summary, we can conclude that both human capital and infrastructure have a significant effect on poverty. However, we will need to explore further if there is a statistical difference between the effects of the two variables. Upon closer examination of the two beta coefficients, we find no statistically significant difference between the effects of the two explanatory variables.

Linear hypothesis test

Response SDG 5:

When we look at the response of SDG 5, we notice that Human Capital Index has a statistically significant impact on SDG 5, whereas Infrastructure Index does not. The value of the coefficient magnitude for hci_ind (52.15) is also larger than the coefficient for infr_ind (-8.30). Based on these observations, we can conclude that there is a statistical difference in the effects of human capital and infrastructure on SDG 5.

Response SDG 13:

The summary of the response to SDG 13 suggests that, once again, infrastructure does not have a statistically significant effect, but the impact of human capital is significant. So, we can claim that human capital has a statistically more significant effect on SDG 13 Climate action. However, we should also note that the coefficients are negative, which means there is a negative correlation between human capital and SDG 13. This is consistent with the basic correlations of the indicators in our dataset (please, see the Correlation Matrix table). I discuss these findings further in the conclusion.

Correlation Matrix

IV. CONCLUSION

Our findings confirm once again that both human capital and infrastructure are essential for the sustainable development of countries. They are both fundamentally important factors foretelling a country’s level of development. With that being said, based on our results, we can reject the Null Hypothesis that there is no statistical difference in the effect of human capital and infrastructure on a country’s SDG Index. The statistical analysis suggests a stronger inter-dependency between human capital and the SDG Index than with infrastructure. So, policy-makers facing the dilemma of choosing between investments in human capital and infrastructure should prioritize human capital if their goal is to advance the overall sustainable development agenda in the country.

However, we also found that Human Capital Index and the Infrastructure Index may have different levels of impact on specific objectives within the UN SDG framework. We discovered that human capital is a statistically significant indicator of a country’s performance on SDG 5: Gender equality, whereas infrastructure is not. We also established that while both indicators have a significant impact on a country’s performance on SDG 1: No poverty, there is no statistically significant difference between the effects of human capital and infrastructure on the poverty levels of a country. Last but not least, we figured that compared to infrastructure, there is a stronger inter-dependency between human capital and SDG 13: Climate action. However, there is a negative correlation between human capital and a country’s performance on climate indicators. This should not come as a surprise because developed countries with higher Human Capital Indexes produce far more carbon footprint than developing countries.[11] It is another reminder that developed countries should transition to more sustainable solutions.

V. WORKS CITED

Guterres urges countries to recommit to achieving SDGs by 2030 deadline. (2023, April 25). UN News. https://news.un.org/en/story/2023/04/1136017

Johnson, J. B., & Joslyn R. A. (1991). Political Science Research Methods: Second Edition. Congressional Quarterly Inc.

Merriam-Webster. (n.d.). Infrastructure. In Merriam-Webster.com dictionary. https://www.merriam-webster.com/dictionary/infrastructure

Sachs, J.D., Lafortune, G., Kroll, C., Fuller, G., Woelm, F. (2022). From Crisis to Sustainable Development: the SDGs as Roadmap to 2030 and Beyond. Sustainable Development Report 2022. https://dashboards.sdgindex.org/downloads

The Investopedia Team. (2023, February 7). Infrastructure: Definition, Meaning, and Examples. Investopedia. https://www.investopedia.com/terms/i/infrastructure.asp

World Bank Group. (2023). The Human Capital Project: Frequently Asked Questions. In World Bank. https://www.worldbank.org/en/publication/human-capital/brief/the-human-capital-project-frequently-asked-questions

The World Bank Group. (2020, September 23). Data Catalog. Human Capital Index. https://datacatalog.worldbank.org/search/dataset/0038030 

The world’s top 1% of emitters produce over 1000 times more CO2 than the bottom 1% – Analysis – IEA. (n.d.). International Energy Agency.

https://www.iea.org/commentaries/the-world-s-top-1-of-emitters-produce-over-1000-times-more-co2-than-the-bottom-1

United Nations. (n.d.). The 17 goals: Sustainable Development. United Nations. https://sdgs.un.org/goals

Vehkalahti, K., & Everitt, B. S. (2020). Multivariate Analysis for the Behavioral Sciences: Second Edition. CRC Press.

[1] Guterres urges countries to recommit to achieving SDGs by 2030 deadline. (2023, April 25). UN News.

[2] Sachs, J.D., Lafortune, G., Kroll, C., Fuller, G., Woelm, F. (2022). From Crisis to Sustainable Development: the SDGs as Roadmap to 2030 and Beyond. Sustainable Development Report 2022.

[3] United Nations. (n.d.). The 17 goals: Sustainable Development. United Nations. https://sdgs.un.org/goals

[4] Ibid

[5] The Investopedia Team. (2023, February 7). Infrastructure: Definition, Meaning, and Examples. Investopedia.

[6] World Bank Group. (2023). The Human Capital Project: Frequently Asked Questions.

[7] Merriam-Webster. (n.d.). Infrastructure. In Merriam-Webster.com dictionary.

[8] United Nations. (n.d.). The 17 goals: Sustainable Development. United Nations.

[9] Vehkalahti, K., & Everitt, B. S. (2020), p. 295

[10] Johnson, J. B., & Joslyn R. A. (1991), p. 319.

[11] The world’s top 1% of emitters produce over 1000 times more CO2 than the bottom 1% – Analysis – IEA. (n.d.). International Energy Agency

Impact of climate indicators on the carbon footprint of data centers

December 14, 2022June 7, 2024 hpanahovLeave a comment

by Huseyn Panahov and Ryan Powers

1. Introduction

Carbon emissions are usually associated with the fossil fuel and transportation industries, yet our online activities also have a significant carbon footprint. It may seem counterintuitive, but data centers account for around 2% of all global greenhouse gas emissions. It is roughly in line with the global airline industry, and not far behind the chemical and petrochemical industry. Parallelly with the digital revolution, the demand for data centers continues to increase. While many industry leaders in the data center business have pledged to zero carbon emissions by 2030, these server farms still need gigantic amounts of energy to operate. In this research we have collected data about 41 data centers owned by Google and Oracle. We looked primarily at the power efficiency of the data centers and the climate indicators in the local geography. Our findings show that every 10 degrees Fahrenheit drop in the temperature translates to 0.006 point improvement in the Power Usage Effectiveness of the data centers. (1.0 is an ideal PUE indicator, whereas globally most data centers have a PUE around 1.8)

2. Background

There are 2,749 data centers from nearly 3,000 service providers in the United States, and about 5 thousand more around the rest of the world. With no alternative technology on the horizon, data centers are here to stay and will continue to grow in numbers. The three most important factors affecting data center energy efficiency are: design, power source, and climate. Data center design and more importantly equipment age affects power consumption as older servers and cooling systems operate at lower efficiency. Power is typically from a combination of renewable and non-renewable energy sources, and facilities that derive a greater share of power from renewable sources are more efficient. Most state-of-the-art facilities built by the largest providers (Google, Oracle, Facebook etc.) run up to 100% on renewable energy. This is not the case when considering the entire data center population. Finally, climate impacts energy efficiency predominantly because cooler, more temperate climates require less of a data center cooling system.

We sought to measure the energy efficiency of data centers accounting for external climate factors like wind, temperature, and precipitation. Cooling processes to regulate server temperature are the most energy intensive, and our hypothesis was that in colder climates you would observe more efficient energy consumption compared to hotter climates. Next, we present our methodology, data analysis, results, and areas for further research.

3. Methodology

While there are thousands of data centers around the world, most of them do not share information about their energy consumption. We were fortunate to find open information about 22 data centers operated by Google and 19 by Oracle. These are two tech industry leaders and they operate very energy efficient data centers. This means that the impact of local climate factors is even more significant on an average data center than in our study.

Data centers require large amounts of energy and electricity to power and cool the servers. Consequently, choosing the right location for a data center is a complex task, which requires consideration of local temperatures, power infrastructure, environmental architecture, in addition to business factors such as land price, legal environment, and skilled workforce.

There are a number of factors that impact a company’s decision to identify a location for a data center. Below are some of the most important factors:

Table 1: Decision-making factors for choosing a data center location

Non-environmental factors	Description
1. Availability of trained workforce	On average a large data center employs between 50 and 500 employees. They usually need trained workforce who can operate the technology and respond to emergencies.
2. Proximity to the customer base	The shorter the distance between the data center and the main customer base, the less chances for incidents along the route
3. Availability and price of land	Large data centers usually require anywhere between 100’000 and 5’000’000 square feet of land.
4. Tax privileges	On average tech companies invest between $300 million and $3 billion to construct a large database. They provide both short term employment opportunities during the construction phase and long-term jobs after the launch.
5. Security	Are there conflicts or other security vulnerabilities in the area?
6. Rule of law	Can tech companies rely on fair judicial procedures?
7. Energy infrastructure	This can be both environmental and non-environmental, but data centers need large amounts of electric power to remain operational 24/7.

Environmental factors	Description
1. Energy infrastructure	Does the existing energy infrastructure rely on renewable power sources or fossil fuels?
2. Potential for producing renewable energy	Wind speed, sunny days, precipitation
3. Water resources	Besides energy, operating a large data center also requires access to large amounts of water. Water Usage Effectiveness (WUE) is the industry metric to measure the efficiency of data centers in utilizing the water resources
4. Average Temperature	Average temperature
5. Temperature variance	How much temperature varies in various time intervals

Our study focuses solely on environmental factors, specifically how local climate conditions impact a data center’s power efficiency. We are looking for empirical evidence that data centers located in colder climates have higher power efficiency. Then, building up on this analysis we recommend what climate zones would be optimal locations for large data centers.

Every year an increasing number of tech companies release sustainability reports, which analyzes and summarizes the environmental impact of their business operations. However, most companies offer only aggregate numbers and do not make publicly available the datasets that shape those analyses. Big tech companies, such as Amazon and Microsoft, do not share even the locations due to safety considerations. Consequently, availability of data was one of the main factors that shaped this research.

In our project we look at the data centers of two multinational tech companies Google and Oracle. They have made publicly available both the locations of their data centers, as well as the Power Usage Effectiveness (PUE) indicator for each data center. Power Usage Effectiveness is the industry metric to estimate the power efficiency of a data center. Lower PUE means better power efficiency. The lowest possible PUE level is 1.0, which means 100% power efficiency. For most data centers the PUE level varies between 1.2 and 3.0, whereas the industry average is 1.8.

We collected PUE indicators for 38 data centers owned and operated by Google and Oracle and spread across 16 countries and 14 US states. Next, we looked up the various climate indicators for each location at a county or city level. Consequently, we built a dataset with 17 data points for each location, which accounted for local temperature variance, seasonal temperature, average temperatures, precipitation, wind speed, cloudiness, and solar power potential. Please, see the below list for our list of variables:

Table 2: List of variables

#	Variable	Description
1	State	Country or US State where the database located
2	Database location	Location of the database
3	Company	Company that owns the database
4	PUE	Power Usage Effectiveness
5	Temp_variance	The difference between highest and lowest temperatures (max of high monthly average – min of low monthly average) in a given location * *
6	Temp_annual	Average annual temperature **
7	Temp_halfyear_warm	Average temperature Apr – Sep (6 months)
8	Temp_halfyear_cold	Average temperature Oct – Mar (6 months)
9	Temp_winter	Average temperature for Dec – Jan – Feb
10	Temp_spring	Average temperature for Mar – Apr – May
11	Temp_summer	Average temperature for Jun – Jul – Aug
12	Temp_fall	Average temperature for Sep – Oct – Nov
13	Rain_annual	Sum of monthly rain averages. Measured in inches
14	WindSpeed_annual	Average of monthly wind speeds. Measured in mph
15	SolarPower_annual	Average Daily Incident Shortwave Solar Energy for the whole year . Measured in kWh
16	SolarPower_summer	Average Daily Incident Shortwave Solar Energy for Apr – Sep
17	SolarPower_winter	Average Daily Incident Shortwave Solar Energy for Oct – Mar
18	Cloudy_annual	% of the time the weather is cloudy in a year
19	Cloudy_summer	% of the time the weather is cloudy in warmer months: Apr – Sep
20	Cloudy_winter	% of the time the weather is cloudy in colder months: Oct – Mar
* All temperatures are measured in Fahrenheit ** For Australia and Chile, the data points were flipped

4. Data Analysis

3.1 Statistical descriptions

In our dataset we have 20 variables, of which 17 are numeric and 3 are strings. We do not have any missing variables, because we constructed this dataset by hand. Let us look at basic statistical descriptions of our numeric variables.

Table 3: Statistical description of numeric variables

Based on these descriptions we can tell that climate conditions across the data centers in our dataset are quite diverse. For example, the amount of annual rainfall in inches varies between 8 and 73 inches depending on the location. The annual wind speed in these locations varies between 5 mph and 14 mph. The annual temperature varies between 42- and 82-degrees Fahrenheit.

Average temperature at a given location is 59 degrees Fahrenheit. However, considering that in an average data center there are 100 ‘000 servers, where each server emits 1200 BTU heat per hour, which would increase the indoor temperature by 213 degrees Fahrenheit without a proper Heating, Ventilation and Air Conditioning (HVAC) system. Generally, it has been considered that the optimal ambient temperature for most technologies, including servers in the data centers, is 68-75 degrees Fahrenheit.^[1] More recently, some companies have introduced new servers that have higher heat tolerance at 81 degrees Fahrenheit.^[2] With all things considered, it would be reasonable to assume that optimal indoor temperature for an average data center today is 72 degrees Fahrenheit. So, even in coldest locations there is a need for electric power to cool down the internal temperature, as well as to power up the technology.

As we can see the PUE values in our dataset vary from 1.06 to 1.78, while the average PUE is equal to 1.26. So, the average PUE value in our dataset is about 30% lower than the industry average PUE, which equals 1.8. This means that overall, the dependance on climate factors is likely to be higher for data centers than in our data set, because higher power efficiency (lower PUE) also means relatively less dependence on climate.

Picture 1: PUE distribution

3.2 Correlations

Now, let us look at the correlation between our numeric variables. We can construct a heatmap. We can see that there is a strong negative correlation -0.73 between SolarPower_annual and Cloud_annual, which validates the credibility of our dataset. Naturally, there should be a negative correlation between cloudy weather and the potential for solar power. However, most importantly we want to check the correlations between PUE and other variables.

We want to identify which variables have the most significant correlation with PUE. We notice that there is a positive correlation between PUE and annual temperature average (Temp_annual). There is an even stronger Temp_halfyear_cold (temperature for October through March), and PUE at the level of 0.4.

Picture 2: Heat-map of numeric variables

There is also a moderately strong relationship at the level of 0.4 between Temp_winter (average temperature for December, January, February) and PUE. If we look at this relationship separately, we notice that if the average winter temperature in a given location is 30 degrees Fahrenheit or below, then the PUE is most likely to be sub 1.2.

Picture 3: PUE vs Winter temperature

3.3 Building the model

For constructing our final model, we picked only one independent variable: the temperature for the cold half of the year. We could use more variables, but it could lead to multicollinearity and undermine the efficiency of our analysis. We built two models: a linear regression and a decision tree model.

Picture 4: Linear and Decision Tree models

The above visualization on Picture 4 represents the outputs of our models. The orange line represents a linear regression model, while red dots represent the outputs of a decision tree model. Below are the performance indicators of our models. Generally, we want to pick the model with lower Root Mean Square Error (RMSE) and higher r-squared. We notice that in this regard the decision tree model performs much better than the linear model. However, considering that decision tree models tend to be overfitting, we could choose either one of the models. (Note: because we have a very small dataset, we did not split it into training and test data).

R-squared is not as important in this case, because we are not building a predictive model and the difference between the RMSE values is not very significant, so we could choose either one of the models.

5. Conclusions

If we look up the coefficients of the linear regression model, we get the following numbers:

This means that the relationship between PUE and Temp_halfyear_cold, is as follows:

PUE = 0.943 + 0.006 x [Temp_halfyear_cold]

Based on this formula, we can suggest that every 10 degrees Fahrenheit decrease in the temperature for the cold half of the year, leads to a 0.06 decrease in PUE. Based on this formula, average winter temperature of 10 degrees Fahrenheit would mean PUE = 1.03 (0.943 + 0.006*10), a near perfect level of power efficiency. However, we understand that there are few places on earth with such low temperatures and they might not be the best locations for data centers due to a number of other reasons, discussed above.

This analysis provides empirical evidence that data centers have better power efficiency and lower carbon footprint in climates with lower temperatures. It shows that there is a moderately strong relationship between winter temperatures and PUE, and every 10 degrees increase in temperature could lead to about 0.06 decrease in power efficiency.

6. Limitations and future research

Our research was limited by the data we could access. Oracle and Google are two large companies that happen to uniformly report PUE metrics, as most do not. This limiting factor led to us not being able to compare them to other peers such as Facebook, Equinix, Microsoft. Furthermore, Oracle and Google already have a commitment to sustainable data centers, and thus we were unable to incorporate other companies with perhaps less sustainable practices into our dataset.

The PUE metric could also be considered a limitation. It is a metric designed for easy reporting and industry comparison, rather than true efficiency measurement. The input data for the calculation can and does vary company to company, given that no industry regulation mandates how it is measured and reported.

Future research could explore many different avenues. First, our model was not predictive since our dataset was so small. With a larger data set, one could predict the optimal climate for a data center. From there, we could have measured the PUE and carbon emissions differentials by relocating a data center to a more optimal location. Additionally, with more companies represented in the data, we could control for variables like market share, capital expenditures, and investments in renewable energy. Finally, a more robust analysis could identify a superior metric to PUE in measuring and comparing data center efficiency.

7. Sources

Ambient Temperature and Why it Matters for Data Centers. (2022, December 1). History-Computer. https://history-computer.com/ambient-temperature-and-why-it-matters-for-data-centers/

Benoit, R. (2022, February 9). An Updated Look at Data Center Temperature and Humidity. AVTECH. https://avtech.com/articles/4957/updated-look-recommended-data-center-temperature-humidity/

Google. (n.d.). Data Centers. Google. Retrieved December 14, 2022, from https://www.google.com/about/datacenters/

Oracle Cloud Data Center regions and locations. Oracle. (n.d.). Retrieved December 14, 2022, from https://www.oracle.com/cloud/cloud-regions/data-regions/

Siddik, M. A., Shehabi, A., & Marston, L. (2021). The environmental footprint of data centers in the United States. Environmental Research Letters, 16(6), 064017. https://doi.org/10.1088/1748-9326/abfba1

The Weather Year Round Anywhere on Earth – Weather Spark. (n.d.). https://weatherspark.com

United States of America: Data center market overview. Cloudscene. (n.d.). Retrieved December 14, 2022, from https://cloudscene.com/market/data-centers-in-united-states/all

[1] Ambient Temperature and Why it Matters for Data Centers. (2022, December 1). History-Computer.

[2] Benoit, R. (2022, February 9). An Updated Look at Data Center Temperature and Humidity. AVTECH.

Blockchain solution to advance conscious consumerism

October 30, 2022June 20, 2023 hpanahovLeave a comment

Introduction

A couple of hundred years ago, an average person’s food source radius was around 10 miles. Today our food basket is a product of a complex web of farmers, freighters, trailers, retailers, and suppliers, that stretches into thousands of miles long supply chains. In the last few years, many ideas have emerged about how to improve the status quo in the supply chain domain. When the COVID-19 pandemic put the global supply chains to the test, it exposed some fundamental shortcomings, especially in the food industry, and emphasized the need for new solutions. One of the most innovative and widely discussed ideas is the application of blockchain technologies, which has captured the public imagination since 2009. This paper looks at the application of this new technology in the food industry from the perspective of customers and discusses its potential perspective and cons.

Blockchain

Blockchain is software that creates a network of computers for the authentication and verification of digital documents. It is also called Distributed Ledger Technology due to its core idea, which is for various blocks of information in a chain of computers to keep tabs on each other or maintain autonomous ledgers of transactions to avoid duplications or double-spending. The concept of a blockchain protocol was first put forward in 1972 by an American computer scientist David Lee Chaum in his dissertation work “Computer Systems Established, Maintained, and Trusted by Mutually Suspicious Groups,” where he argued that “a number of organizations who do not trust one another can build and maintain a highly secured computer system,” or network of vaults, they can all trust (Chaum, 1972). However, this idea did not attract much attention from any industry until the release of Bitcoin cryptocurrency decades later. When the unknown author (or authors), under the pseudonym Satoshi Nakomoto, published the white papers for Bitcoin in October 2008, the blockchain technology that enables it stirred a lot of attention. Even people skeptical about cryptocurrency became intrigued about other potential applications of the technology that powers it.

Supply Chain

One of the most notable books about the global impact of this technology is titled Blockchain Revolution, written by Don and Alex Tapscott. In the book, first published in 2016, the authors remark, “We often get asked, “What is the next big killer app for blockchain?… There is no better candidate than the global supply chain, an industry that runs two-thirds of the global economy” (Tapscott & Tapscott, p. 5). Modernization of supply chain operations and management is long overdue, and in recent years exciting ideas have emerged about using blockchain applications to address some of the most salient issues in this domain. In a short period, dozens of new startups and several large corporations started looking for ways to leverage the potential of blockchain applications for improving supply chain management. For example, in 2017, Walmart, along with some of its biggest suppliers in the food category, such as Dole, Kroger, McCormick, Nestlé, Tyson Foods, and Unilever, developed a partnership with IBM to use blockchain for food traceability (Sristy, 2017). Since the COVID-19 crisis, which exposed some of the fundamental issues in the status quo of the supply chains, especially in the food industry, the appetite for innovative solutions has grown even larger.

What would it mean for consumers?

Government and businesses have a common interest in the consumer, with the private sector wanting to earn repeat business from customers and the government wanting products to be safe for consumption. If blockchain becomes a mainstream solution in supply chains, it would create more transparency and make it easier to track food products not only for business operators and government regulators but also for customers. According to Walmart, in 2016, it took their food safety specialists 6 days, 18 hours, and 26 minutes to track where a package of sliced mangoes came from (Sristy, 2017). However, with the help of blockchain applications, they were able to cut it down to 2 seconds. It is as easy as scanning a barcode, and it means even customers in a store can pull out a phone, scan a product and see the entire life story of a given product.

Considering the increasing customer interest in learning more about their food basket, blockchain could mean a win both for consumers and businesses who want to gain a marketing edge. According to a report released in 2022 by FMI – Food Industry Association and NielsenIQ, 81% of shoppers say “transparency is important or extremely important to them both online and in-store” (NielsenIQ & The Food Industry Association, 2022). This growing demand for more information is why we see more and more labels on products such as Animal Welfare Approved, Cruelty-Free, Certified Naturally Grown, Fair Trade, etc. Today, there are hundreds of food certification labels in the US that non-profit, public organizations issue. Most of these entities are well-intentioned, but they do not always have the resources/capacity to enforce high standards. For example, a Peruvian Fairtrade-certified coffee producer told Financial Times, “There is no way to enforce, control and monitor – in a remote rural area of a developing country – how much a small farmer is paying his temporary workers” (Weitzman, 2006). So blockchain could help these certificate-issuing entities to keep the customers better informed.

One successful blockchain startup Banqu, used in 50 countries, makes even the smallest farmers in remote areas bankable, as they receive a text message with a unique code after every transaction, which they present to local banks and then receive their money. It creates unprecedented visibility for small farmers and significantly reduces the opportunities for fraud. We live in an interconnected world, where small farmers in rural areas of distant countries play a role in shaping our daily food basket. Banqu (a wordplay of bank and you) suggests that with their application, “you will know who harvests your crops, who collects your waste for recycling, and who mines your gems and minerals,” and claims that companies that partner with them perform better on climate action, poverty alleviation and human rights (Banqu, n.d.). Blockchain could shed light of transparency along the entire line of the supply chains and make visible even the smallest farmers all the way upstream to the final destination consumers.

Conscious consumerism or consumer activism are trends that are expected to grow in influence over time. New generations are more likely to leverage their individual purchasing choice and mobilize over social media platforms for collective action against corporate practices they disagree with. According to LendingTree, in 2020, 38% of Americans boycotted a company, mainly due to a political or covid-19 related issue. This number was 51% and 52% for Gen Z and millennials, while it averaged 22% and 16% for baby boomers and the silent generation (Holmes, T. 2020). As one example, in 2020, consumers from around the world mobilized behind the #payup campaign that forced large brands such as Gap, Levi’s, Zara, Nike, Nike, H&M, and Ralph Lauren, among others, to pay up wages to employees in Bangladesh, as they were facing cancellations of orders north of $15 billion (Bobb, 2020). Thus, there is growing customer interest in the products and how those products reach the store shelves, and blockchain could be an effective method both for consumers and civil society organizations to investigate that process and make their choice.

Limitations

Blockchain is a powerful technology with tremendous potential for driving positive changes in supply chain management, but it is not a solution that works magically on its own. There are a number of limitations that need to be addressed. First, we are better informed about the successful applications of blockchain, whereas most blockchain initiatives have failed (Alighieri, 2019). So, businesses need to study both successes and failures in order to identify the optimal implementation plan.

The second shortcoming is that blockchain is not the only solution, and there could be less complex remedies to address the current issues in supply chains – the argument being that blockchain is a big burden with high alternative costs and low benefits. For example, in 2017, McKinsey released a study titled “Blockchain technology for supply chains—A must or a maybe?”, which suggests that “well-managed central databases with good data management, combined with supply-chain visualization and analytical prowess, can be achieved at scale today,” so there is “a good-enough solution without blockchain” (Alicke et al., 2017). However, blockchain generates more reliable real-time data that can be verified, with the cost to attain this data decreasing as more adopt blockchain.

Lastly, another potential impact of blockchain technology is that it could create closed ecosystems that are not inclusive and skew the level playing field for smaller businesses. In the federal government, the Department of Homeland Security (DHS) has already taken the initiative to lead the efforts against the potential “walled gardens” effects of blockchain solutions. According to the Science and Technology Directorate under the DHS: “The challenge with blockchain technology is the potential for the development of “walled gardens” or closed technology platforms that do not support common standards for security, privacy, and data exchange… this would limit the growth and availability of a competitive marketplace of diverse.” (Blockchain Portfolio, 2022). To avoid the trap of “walled gardens,” there is a need for both public-private partnerships, as well as cross-disciplinary collaboration among legal experts, computer scientists, and business experts.

Conclusion

In the last several years, we have experienced profound changes in last-mile delivery, where customers can track their orders live with updates at every stage of the transit process. However, the upstream supply chain networks have mostly stayed the same for decades. Farmers, container carriers, railways, and large trucks have relied on traditional methods and paper transactions to manage their operations. Blockchain applications could be the turning for stakeholders in the upstream supply chains to step into the modern age. A distributed ledger technology can deliver several benefits to businesses, such as higher risk resiliency due to food traceability. Widespread application of blockchain technologies would also facilitate the job of federal agencies to enforce higher food standards and respond to crises, such as food-borne illness outbreaks. Finally, customers also stand to benefit from more transparent food supply chains, especially in light of trending conscious consumerism.

References

Alicke, K., Davies, A., & Leopoldseder, M. (2017, September 12). Blockchain technology for supply chains–A must or a maybe? McKinsey. Retrieved October 11, 2022, from https://www.mckinsey.com/capabilities/operations/our-insights/blockchain-technology-for-supply-chainsa-must-or-a-maybe

Alighieri, D. (2019, May 20). Why Enterprise Blockchain Projects Fail. Forbes. Retrieved October 12, 2022, from https://www.forbes.com/sites/dantedisparte/2019/05/20/why-enterprise-blockchain-projects-fail

Banqu. (n.d.). BanQu: About. Supply Chain Software – Blockchain Platform. Retrieved October 11, 2022, from https://banqu.co/

Blockchain Portfolio. (2022, January 8). Blockchain Portfolio | Homeland Security. Retrieved October 10, 2022, from https://www.dhs.gov/science-and-technology/blockchain-portfolio

Bobb, B. (2020, July 10). Garment Workers Are Finally Getting Paid The Billions They’re Owed From Brands Like Gap and Levi’s. Vogue. Retrieved October 11, 2022, from https://www.vogue.com/article/remake-payup-campaign-social-media-garment-workers-wages-gap

Chaum, David Lee. 1972. Computer Systems Established, Maintained and Trusted by Mutually Suspicious Groups. University of California, Berkeley

Holmes, T. (2020, July 20). 38% of Americans Are Currently Boycotting a Company, and Many Cite Political and Coronavirus Pandemic-Related Reasons. Lending Tree. Retrieved October 11, 2022, from https://www.lendingtree.com/credit-cards/study/boycotting-companies-political-pandemic-reasons/

Hyperledger Foundation. (n.d.). Walmart Case Study – Hyperledger Foundation. Hyperledger Foundation. Retrieved October 10, 2022, from https://www.hyperledger.org/learn/publications/walmart-case-study

Iakovou, E. I., & White III, C. (2022, September 14). A data-sharing approach for greater supply chain visibility. Brookings Tech Stream. Retrieved October 6, 2022, from https://www.brookings.edu/techstream/a-data-sharing-approach-for-greater-supply-chain-visibility/

NielsenIQ and FMI – The Food Industry Association. (2022, January 25). Transparency in an evolving omnichannel world. NielsenIQ. Retrieved October 11, 2022, from https://nielseniq.com/global/en/insights/report/2022/transparency-in-an-evolving-omnichannel-world/

Sristy, A. (2017, August). Blockchain in the food supply chain – What does the future look like? Walmart One. Retrieved October 11, 2022, from https://one.walmart.com/content/globaltechindia/en_in/Tech-insights/blog/Blockchain-in-the-food-supply-chain.html

Tapscott, D., & Tapscott, A. (2018). Blockchain Revolution: How the Technology Behind Bitcoin and Other Cryptocurrencies Is Changing the World. Penguin Publishing Group.

Walmart Case Study – Hyperledger Foundation. (n.d.). Hyperledger Foundation. Retrieved October 10, 2022, from https://www.hyperledger.org/learn/publications/walmart-case-study

Weitzman, H. (2006, September 8). The bitter cost of ‘fair trade’ coffee. Financial Times. Retrieved October 11, 2022, from https://www.ft.com/content/d191adbc-3f4d-11db-a37c-0000779e2340

A Critical Review of UNEP’s Food Waste Index

April 5, 2022May 26, 2025 hpanahovLeave a comment

Its Impact and Limitations on Sustainable Consumption Policies

I. Introduction

Sustainable consumption is one of the priority areas in the international development agenda. In 2015, 193 UN member states undersigned the 2030 Agenda for Sustainable Development, which consists of seventeen interlinked Sustainable Development Goals. It is a comprehensive development framework that also focuses on “responsible consumption and production.” However, it is a strategic-level document, which did not take into account the operational-level challenges for developing indicators to measure the progress towards these goals. In 2021, United Nations Environment Program published its first Food Waste Index (FWI) report, which is presented as the most comprehensive report on global food waste and made many news headlines.[1][2] The UNEP has done an enormous job building the groundwork for producing global data on food waste, but the organization attributes low or very low confidence level to nearly 80% of the data used to construct the FWI. Given the context, the FWI is not a reliable benchmark for either measuring progress or informing adequate policy decisions.

II. Background

In September 2015, at the landmark UN Sustainable Development Summit in New York, countries worldwide agreed on a post-2015 global development agenda “to achieve a better and more sustainable future for all people and the world by 2030.”[3] They agreed on 17 Sustainable Development Goals, which are broken down into 169 SDG Targets, which in turn have 232 unique indicators (as of February 2022) to track progress.[4] Particularly, SDG 12 focuses on “responsible consumption and production,” which is about “decoupling economic growth from environmental degradation, increasing resource efficiency and promoting sustainable lifestyles.”[5] There are eight targets under SDG 12, which mainly focus on national policies and big-scale producers, but two of them are about consumer behavior and thus fall within the scope of our research. Target 12.3: reduce food losses along production and supply chains and halve global per capita food waste at the retail and consumer levels;[6] and, 12.8: promote universal understanding of sustainable lifestyles.

SDG Target 12.3 has two indicators: the Food Loss Index produced by Food and Agriculture Organization of the UN and Food Waste Index produced by the UN Environment Programme (UNEP). The Food Loss Index (FLI) measures the percentage of food loss from production up to (but not including) retail level. Food Waste Index (FWI) focuses on the percentage of food wasted at the retail and consumption stages. Since the focus of this paper is on sustainable consumption, I will take a closer look at the Food Waste Index, analyze the data behind it, and assess its impact.

After carefully examining the datasets used for the Food Waste Index, I concluded that existing data are not reliable enough for measuring the progress towards SDG Target 12.3, and advancing tailored policy interventions. However, these conclusions should not undermine the importance of the food waste issue, since every data point, every study and observation demonstrate that there is a significant food waste problem both in economically developed and underperforming countries. It is a major concern, as hundreds of millions around the world suffer from malnutrition, since their caloric intake falls below minimum energy requirements.[7] That is also the reason, why we need to understand the limitations of currently available data.

III. Data Analysis

UNEP worked together with a non-profit organization based in the United Kingdom the Waste and Resources Action Program (WRAP) to produce its first Food Waste Index in 2021, which is considered the “most comprehensive report into global food waste in homes.”[8] The report was published in 2021, but the numbers represent the situation in 2019. According to the report, 17% of all food that reaches retail ends up in the dumpster. Of that number, households are accountable for 61% of food waste, food service industry (restaurants) for 26% and retail for 13%.[9]

These are staggering numbers and to put them in perspective, they mean that roughly 931 million tonnes of food is wasted every year, which is more than the total consumption in a country as big as India. If we combine Food Waste Index with Loss Index, it would mean that more than a third of all food is either lost or wasted somewhere along the chain, which also accounts for nearly 10% of global carbon emissions. However, what if we scratch the surface and look behind the report into the raw data[10] that shaped this report. How reliable are the food waste numbers?

Authors of the report acknowledge that it is very challenging to collect data on food waste and admit that they have high-quality data from only 14 countries,[11] while they have medium confidence in reports from 42 countries. The dataset of the report lists 233 geographic units (mainly UN Member states), and has assigned no estimate, very low confidence or low confidence for data estimates for 183 of them, or 79%.[12] The below pie chart presents a visual breakdown of the data source confidence levels:[13]

Evidently, there is not much confidence in the credibility of the reported figures. The authors of the report also elaborate that overall, they were able to collect 152 data points from 54 countries and then extrapolated that data to calculate the estimates for other geographic areas where data was not available. However, even the credibility of those available data points can be questioned. For example, Poland is assigned a medium confidence level, even though the data source for Poland is a small study by local civil society actors. “The Pilot Study of Characteristics of Household Waste Generated in Suburban Parts of Rural Areas” (Steinhoff-Wrześniewska, Aleksandra), mentions that:

21 households, representing 83 people, were audited. None of them were involved in agricultural production. They were provided with three bags for sorting (bio-waste, hygenic waste, all other waste) and had waste collected in each of the four seasons. It is unclear for how long during each season the measurement took place. As a result of small sample size and unknown length, we cannot have high confidence in the estimate.

Population of Poland is 38 million and only 15 million of it lives in rural areas, while 61% reside in urban centers. A sample of only 21 households from suburban parts of rural Poland observed over undefined periods of time is not a strong representative of food management habits across the whole country.

The question is whether these numbers can serve as a reliable metrics to measure the progress or calibrate policy actions. SDG Target 12.3 aims to halve the global per capita food waste by 2030. According to UNEP’s 2021 Index average food waste per household equals 79 kg a year in high-income countries equals, 76 kg in upper middle-income countries, 91 kg in lower middle-income countries, while the data for low-income countries is insufficient. For example, the 2021 Food Waste Index Report mentions that “The next questionnaire will be sent to Member States in September 2022, and results will be reported to the SDG Global Database by February 2023.” What if the next report shows that annual food waste per household in upper middle-income countries is 86 kg. It would lead to the conclusion that the food waste in this category of countries is increasing, while in fact, the number could have been decreasing. American biochemist Erwin Chargaff once said: “I thought it was the task of the natural sciences to discover the facts of nature, not to create them.” Relying on inaccurate data for measuring progress could set in motion mismatched policy interventions and do more harm than good.

IV. Theoretical Framework

There are no easy shortcuts to producing global data, such as Food Waste Index. It requires the formation of a specific global knowledge infrastructure focused around food waste. It entails standardizing measurements and processes, disciplining staff and synchronizing reporting timelines. Achieving this subject specific institutional interoperability on a global scale, requires significant amounts of money and resources. So, I explain the current shortcomings of the Food Waste Index, by looking at the global knowledge infrastructure behind it and reference mainly these two scholarly works for theoretical backup: A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming, by Paul Edwards, and Standards and Their Stories: How Quantifying, Classifying, and Formalizing Practices Shape Everyday Life, by Martha Lampland and Susan Leigh Star.

Food Waste Index is not a legitimate scientific fact, because there is no well-founded knowledge infrastructure behind it. In his book “A Vast Machine”, Paul Edwards writes that “an established fact is one supported by an infrastructure,”[14] and elaborates that “knowledge infrastructures comprise robust networks of people, artifacts, and institutions that generate, share, and maintain specific knowledge about the human and natural worlds.”[15] If we get rid of the infrastructure, we are left with claims and facts that can neither be backed up nor verified.

In modern world, infrastructures are all around us and we use them on a daily basis, without paying much attention, unless there is a problem with them and/or we have to change them.[16] For example, behind the tap water we use, there is a complex infrastructure of plumbing and water regulation. In a similar fashion, global data requires an elaborate knowledge infrastructure that consists of national communities of scientists, government bureaucrats, and civil society activists, who understand each other, can inform and keep each other accountable. These communities need physical facilities, such as offices and laboratories, as well as legal space to conduct their work with respect to intellectual property.[17] They require mediums of communication such as conferences, journals, web portals etc., to exchange knowledge and keep up to date.

However, most importantly, for these national information eco-systems to reach beyond their borders and co-produce global data, they need standardized methods and measures. The amount of reported food waste can change depending on how countries define food waste, when they measure it and what factors they take into account. For example, according to the UNEP, “food waste is defined as edible parts and associated inedible parts going directly to the following destinations: landfill, controlled combustion, litter discards/refuse, compost/aerobic digestion, land application, co/anaerobic digestion, sewer, but does not include food waste used for biomaterial/processing, animal feed or not harvested.[18] In some countries associated inedible parts of the food used for compost, might not be considered food waste. A more accurate report, should also take into account seasonal fluctuations of food waste.

V. UNEP’s Food Waste Index

Bottom line up front, there is no global knowledge infrastructure around food waste and UNEP did not have the resources to build it up in the given time frame. UNEP has been working on food waste reduction since 2013, when it launched the global campaign Think Eat Save, but it became a priority task for UNEP only in 2019, following the UN Environment Assembly Resolution 4/2, which mandated UNEP to accelerate global action on food waste reduction.[19]

Established in 1972 and headquartered in Nairobi, Kenya UNEP has around 860 staff members worldwide.[20] The mission statement of the UNEP, which is celebrating its 50^th anniversary this year, “is to provide leadership and encourage partnership in caring for the environment by inspiring, informing, and enabling nations and peoples to improve their quality of life without compromising that of future generations.”[21] By default the top priority for UNEP has been to lead the international efforts against climate change.

In 2013, UNEP in partnership with the Food and Agriculture Organization of the UN (FAO) launched the Save Food Initiative and its subcomponent program “Think Eat Save: Reduce Your Footprint.” Primary goal of the FAO established in 1945 is to “achieve food security that people have regular access to enough high-quality food to lead active, healthy lives.”[22] In 2011, FAO had released its estimates that nearly 1/3 of the world’s food was lost or wasted every year, which lead to their joint Save Food Initiative with UNEP two years later.

So, until recently food waste data was tangled with research into food loss and fell under the prerogative of FAO. The inherent structure of the UN system and the scheme for resource distribution, incentivizes UN agencies to compete for more responsibilities and programmatic oversight. In a 2019 survey by the UN Office of Internal Oversight Services, 80% of UNEP staff “noted that there was critical competition for donor sources with other UN entities.”[23] This institutional contest between FAO and UNEP could potentially explain why between 2015 and 2019, no organization was assigned as a custodian for Food Waste Index.

The first-time that food waste showed up in UNEP’s program of work and budget was in biennial 2018-2019, approved by the UN Environmental Assembly of the UNEP (UNEA) in May 2016.[24] It includes planned work outputs such as “Within sustainable food and agriculture policy frameworks, urban planning and/or existing sustainable consumption strategies, technical and policy guidance provided to public and private actors to measure, prevent and reduce food waste and increase the uptake of sustainable diet strategies and activities,” as well as “Outreach and communication campaigns to raise awareness of citizens (particularly young people) on the benefits of shifting to more sustainable consumption and production practices.” Their previous work plan for 2016-2017, proposed in 2014, had no mention of food waste.[25]

In May 2016, UNEA also adopted a resolution on “Prevention, reduction and reuse of food waste,” which requests the UNEP Executive Director “in cooperation with the Food and Agriculture Organization to “continue to raise awareness of the environmental dimensions of the problem of food waste, and of potential solutions and good practices for preventing and reducing food waste and promoting food reuse and environmentally sound management of food waste.”[26] However, UNEP became the custodian of the Food Waste Index only in 2019, and solidified itself as the lead agency on tackling food waste pursuant to the UNEA Resolution 4/2.[27]

In 2019, UNEP received a new Executive Director Inger Anderson, a competent professional who is well versed both in sustainable development and food security issues. She has more than 30 years of experience in international development organizations, which include her roles as Vice President of the World Bank for Sustainable Development and Head of the CGIAR Fund Council.[28] CIAGR is the Consortium of International Agricultural Research Centers, which brings together international organizations engaged in research about food security. Her predecessor came from a diplomatic background and was asked to resign as a result of an internal audit. Media reports, citing the leaks from the internal audit documents, mentioned that the head of UNEP spent “$500,000 on air travel and hotels in just 22 months, and was away 80% of the time.”[29] So, positive changes happened in the organization under the new leadership and Food Waste Index became one of the top priorities for UNEP.

When UNEP was first assigned as a custodian in 2019, Food Waste Index was still classified as a Tier 3 indicator by the UN’s Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs). The UN breaks down all SDG indicators into 3 Tiers:

“Tier 1: Indicator is conceptually clear, has an internationally established methodology and standards are available, and data are regularly produced by countries for at least 50 per cent of countries and of the population in every region where the indicator is relevant.
Tier 2: Indicator is conceptually clear, has an internationally established methodology and standards are available, but data are not regularly produced by countries.

Tier 3: No internationally established methodology or standards are yet available for the indicator, but methodology/standards are being (or will be) developed or tested.”

Tier classifications change over time as the quality of data for indicators improves. For example, as of February 2022, IAEG-SDG lists 136 Tier I indicators, 91 Tier II indicators and 4 indicators that have multiple tiers (different components of the indicator are classified into different tiers),[30] while in September 2016, there were 81 Tier I indicators, 57 Tier II indicators and 88 Tier III indicators.[31] According to the IAEG reports Food Waste Index was upgraded from Tier III to Tier to II within 2 years.

When we look at the work plan of the UN Environment Program for 2020-2021, it has 7 subprograms, and collecting data for Food Waste Index falls under the Subprogram 6, which is about Resource Efficiency. In 2020-2021, UNEP allocated $95.6 million to the Subprogram 6, which means roughly $48 million per annum. It had 114 staff members working towards the 20 planned work outputs under the Resource Efficiency subprogram.

Mainly these work outputs were geared towards developing the information infrastructure for delivering the SDG indicators. For example, “Resource use assessments and related policy options are developed and provided to countries to support planning and policy-making, including support for the application and monitoring of relevant SDG indicators.” Or, “Database services providing enhanced availability and accessibility of life cycle assessment data are provided through an interoperable global network, methods for environmental and social indicators and the ways to apply them in decision-making.”[32] Most of these programmatic activities are about capacity development, technical assistance, training, policy support, etc.

As a result of UNEP’s active engagement, the number of countries that have a common global measurement approach for consistent reporting under SDG 12.3 increases every year. On average UNEP adds around 10 countries a year to their list of countries compatible for food waste reporting. This shows that UNEP is on the right track on building the knowledge infrastructure for a more reliable global Food Waste Index.

UNEP’s methodology for data collection is to send out Questionnaire on Environment Statistics (Waste Section) to National Statistical Offices and Ministries of Environment. If the respective authorities from these countries do not respond, then UNEP refers to alternative sources for information. However, we should be clear eyed that national executive agencies that collaborate with UNEP are not politically neutral entities and their responses to questionnaires can be subject to political interests of their respective governments.[33] So, these agencies might have the capacity to produce reliable numbers, but not the intention. For this reason, it would benefit the credibility of the food waste index, if UNEP increases its engagement with civil society organizations that can serve as alternative sources of reporting on food waste.

VI. Conclusion

The 2021 Report on Food Waste Index, does not just provide us with numbers about food waste, but it also informs us about the state of the knowledge infrastructure around food waste. The formation of a knowledge infrastructure is a lengthy and complicated process. Institutional resources of the UN system, its global reach, and modern technologies have enabled UNEP to make tremendous progress towards building this infrastructure, within a very short period of time. However, it is still unclear, when UNEP will be able to produce reliable global data on food waste. UNEP can draw many valuable lessons from their 2021 report on food waste, but it should not be used as a benchmark for progress, since it could lead to many misplaced conclusions down the road.

Looking into the future the importance of sustainable consumption will only increase. Over the course of the past century, humanity experienced unprecedent growth in global wealth and food production. Surging food production rates create enormous pressure on the environment, even though hundreds of millions are still not getting their fair share. One of the big reasons for this failure is the food waste problem. Unfortunately, until recently food waste issue has been largely neglected and calculating exactly how much food is wasted has remained an elusive target. If UNEP stays consistent with its action plan, global Food Waste Index will become increasingly more reliable, as more and more countries will be able to plug into the global knowledge infrastructure on food waste. However, there is a lot of work ahead. In the meantime, I would like to reiterate the call of the UNEP Executive Director Inger Anderson’s opening message in the 2021Food Waste Index Report, “let us all shop carefully, cook creatively and make wasting food anywhere socially unacceptable.”

[1] “U.N. Report Says 17% of Food Wasted at Consumer Level.” U.S., Reuters, 4 Mar. 2021,

[2] Merchant, Natalie. “Global Food Waste Twice the Size of Previous Estimates.” World Economic Forum, 26 Mar. 2021.

[3] Sustainable Development. (2022). UN Department of Economic and Social Affairs. https://sdgs.un.org/

[4] “Measuring Progress towards the Sustainable Development Goals.” Our World in Data, SDG Tracker, sdg-tracker.org. Accessed 5 Mar. 2022.

[5] Sustainable consumption and production policies. (2022). UNEP – UN Environment Programme.

[6] UNEP Food Waste Index Report 2021. (2021). UNEP – UN Environment Programme. https://www.unep.org/resources/report/unep-food-waste-index-report-2021

[7] Roser, M. (2019, October 8). Hunger and Undernourishment. Our World in Data. https://ourworldindata.org/hunger-and-undernourishment

[8] “New UNEP Report Developed in Collaboration with WRAP Reveals True Scale of Global Food Waste.” The Waste and Resources Action Programme, 2021, wrap.org.uk/FoodWasteIndex.

[9] UNEP Food Waste Index Report 2021. (2021). UNEP – UN Environment Programme.

[10] SDG Indicators Database. (2021). UN Department of Economic and Social Affairs. https://unstats.un.org/sdgs/UNSDG/IndDatabasePage

[11] According to the UNEP Food Waste Index Report 2021, countries with high-quality data on food waste are Australia, Austria, Canada, China, Denmark, Estonia, Germany, Ghana, Italy, Malta, the Netherlands, New Zealand, Norway, the Kingdom of Saudi Arabia, Sweden, the United Kingdom and the United States.

[12] “Food Waste Index Level 1 Annex.” UNEP- UN Environment Program, 2021, wedocs.unep.org/bitstream/handle/20.500.11822/35355/FWD.xlsx.

[13] Ibid

[14] Edwards, P. N. (2013). A Vast Machine, p. 22

[15] Edwards, P. N. (2013). A Vast Machine, p. 17

[16] Lampland, Martha, and Susan Leigh Star. Standards and Their Stories.

[17] Ibid

[18] UNEP Food Waste Index Report 2021. (2021), p. 14

[19] “Promoting Sustainable Practices and Innovative Solutions for Curbing Food Loss and Waste.” United Nations Environment Assembly, UNEP – UN Environment Programme, Mar. 2019, wedocs.unep.org/bitstream/handle/20.500.11822/28499/English.pdf.

[20] UNEP | International Organizations. (2005). IGPN – International Green Purchasing Network. http://www.igpn.org/global/interorg/unep.html

[21] “About UN Environment Programme.” UNEP – UN Environment Programme, http://www.unep.org/about-un-environment. Accessed 5 Mar. 2022.

[22] “About FAO.” Food and Agriculture Organization of the United Nations, http://www.fao.org/about/en. Accessed 5 Mar. 2022.

[23] Ivanova, Maria (Feb 23, 2021). The Untold Story of the World’s Leading Environmental Institution: UNEP at Fifty, p. 62

[24] “Programme of Work and Budget for the Biennium 2018‒2019.” United Nations Environment Assembly, UNEP – UN Environment Program, May 2016

[25] “Proposed Biennial Programme of Work and Budget for 2016–2017.” United Nations Environment Assembly, UNEP – UN Environment Programme, June 2014

[26] “Prevention, Reduction and Reuse of Food Waste.” United Nations Environment Assembly, UNEP – UN Environment Program, May 2016.

[27] “Promoting Sustainable Practices and Innovative Solutions for Curbing Food Loss and Waste.” United Nations Environment Assembly, UNEP – UN Environment Programme, Mar. 2019.

[28] Inger Andersen. (2019). UNEP – UN Environment Program

[29] Carrington, D. (2018, November 20). UN environment chief resigns after frequent flying revelations. The Guardian.

[30] “Tier Classification for Global SDG Indicators.” UN Statistics Division, Feb. 2019,

[31] “Tier Classification for Global SDG Indicators.” UN Statistics Division, Sept. 2016,

[32] “Proposed Programme of Work and Budget for the Biennium 2020‒ 20211.” UN Environment Assembly, p. 98

[33] In her book “Shades of Citizenship,” Melissa Nobles presents a very illuminating discussion about the impact of the political interests of the data collecting agencies on the data they produce

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