Preferences of Ukrainian agribusinesses toward sustainability reporting for financing of green recovery

Volodymyr Metelytsia¹, Uliana Gottlieb², Stephan Brosig¹, Taras Gagalyuk¹

¹Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle, Germany

²Swedish University of Agricultural Sciences, Uppsala, Sweden

EAAE| Warsaw, Poland | September 18-20, 2024

Consequences of Russian invasion for agricultural sector of Ukraine

EAAE 2024| CP3b: Session 3b

• 30% of farmland area mine-studded
• Cropland area reduced by 18%
• Grain export reduction by 13% due to Black sea ports blockade
• Added value in agriculture down by 28%
• Currency devaluation by 30%
• Low average value of farmland (EUR 1470 / ha)
• Agricultural losses of USD 40.2 bn

2

www.iamo.de/en

Sustainability reporting is a tool for attracting financing for “green” reconstruction and development of the Ukrainian agricultural sector

EAAE 2024| CP3b: Session 3b

3

Ecological resilience

Social security

Efficient management

Investment attractiveness

ESGI

Martial law

Climatic risks

(EU ETS, CBAM)

EU integration (CAP, Ukraine Facility)

Institutional reforms (land market, public governance, corporate governance)

www.iamo.de/en

The state of sustainability reporting in Ukraine

EAAE 2024| CP3b: Session 3b

4

Warsaw

Stock Exchange

Source: Company ESG Risk Ratings:

https://www.sustainalytics.com/

Source: https://www.kernel.ua/, https://astartaholding.com/

www.iamo.de/en

Utility theory and willingness to accept WTA

EAAE 2024| CP3b: Session 3b

• Lancaster's Consumer Demand Theory (Kelvin Lancaster in 1966) – traditional consumer theory by proposing that consumers derive utility not from goods themselves but from the characteristics or attributes of those goods. According to this theory, each product is seen as a bundle of attributes, and it is these attributes that provide utility to consumers.
• Characteristics:

Attributes Over Goods: Consumers choose goods based on the utility they derive from the combination of attributes those goods offer.

Efficiency in Consumption: Consumers seek to maximize their utility by selecting goods that provide the optimal combination of desired attributes, given their budget constraints.

​

• Willingness to Accept (WTA) is an economic concept used to measure the minimum amount of money an individual or a firm is willing to accept to give up a good, service, or to endure something undesirable, such as environmental damage. WTA is particularly relevant in contexts where a change or loss needs to be compensated, such as in environmental economics, property rights, or labor negotiations.
• Features:

Compensation Requirement: WTA is the lowest price at which someone is willing to sell a good, accept compensation for a loss, or agree to an unfavorable change.

Formula:

The utility-based formula for WTA can be expressed as:

​

Where: U is the utility function, Q₁ is the quantity after the change (e.g., after giving up the good), Q₀ is the quantity before the change, Y is the income or wealth level.

5

Source:

• Lancaster, K. J. (1966). A New Approach to Consumer Theory. Journal of Political Economy, 74(2), 132-157.

Source:

• Hanemann, W. M. (1991). Willingness to Pay and Willingness to Accept: How Much Can They Differ? American Economic Review, 81(3), 635-647.
• Freeman, A. M. (2003). The Measurement of Environmental and Resource Values: Theory and Methods. Resources for the Future.

www.iamo.de/en

Willingness and readiness to attract loans for green modernization through the disclosure of environmental indicators in ESG Report

EAAE 2024| CP3b: Session 3b

6

Table. Choice attributes and attribute levels

www.iamo.de/en

Preparation and publication of environmental indicators:�willingness to accept (WTA)

EAAE 2024| CP3b: Session 3b

​

​

Current:

• Laboratory analyses (water, air, soil)
• Training, courses for accountants
• Services of financial consultants
• Data collection and report preparation
• Audit of the ESG report

Future:

• Periodic measurement, education, preparation and audit of the ESG report

7

Costs of ESG Report

Benefits of ESG Report and “green” loan

​

Short-term

• Savings in interest costs on “green” loans

Long-term

• Additional profits from the higher value/price of low-carbon products
• Additional income from carbon certificates
• Cost savings on carbon taxes, e.g., on exports to the EU
• Image of an enterprise that cares about the environment and the health of the local community

Decision

to accept/

not accept

www.iamo.de/en

Example of a choice card for the pilot survey

EAAE 2024| CP3b: Session 3b

8

• 12 choice cards were generated in Ngene software (Qi, 2002)
• 2 blocks with six cards each

www.iamo.de/en

Pilot survey administration

EAAE 2024| CP3b: Session 3b

9

• Online questionnaire consists of five parts (on the Qualtrics platform)
• In May-June 2024, an online questionnaire was sent to 201 agricultural enterprises in Ukraine
• 65 respondents completed the pilot survey
• 31 participants answered all 36 questions of the pilot survey
• For the choice experiment, we received results from 43 participants

www.iamo.de/en

Pilot data: regions of the survey

EAAE 2024| CP3b: Session 3b

10

www.iamo.de/en

Portrait of the respondent. Area of agricultural land, ha

EAAE 2024| CP3b: Session 3b

11

www.iamo.de/en

Portrait of the respondent. Production specialization

EAAE 2024| CP3b: Session 3b

12

www.iamo.de/en

Portrait of the respondent. Position

EAAE 2024| CP3b: Session 3b

13

www.iamo.de/en

Portrait of the respondent. Age, years

EAAE 2024| CP3b: Session 3b

14

www.iamo.de/en

Multinomial Logit (MNL) Model

EAAE 2024| CP3b: Session 3b

• The Multinomial Logit (MNL) model is one of the most widely used models in discrete choice analysis, which is employed to predict outcomes where individuals or decision-makers choose between multiple discrete alternatives. The MNL model is a particular case of the logit model that is based on the assumption of the Independence of Irrelevant Alternatives (IIA), which states that the relative odds of choosing between any two alternatives are unaffected by the presence of other alternatives.
• Characteristics:
• Choice Probability: The MNL model estimates the probability that a decision-maker chooses a particular alternative from a set of options. The probability of choosing alternative j is given by:

​

where V_ij is the deterministic component of the utility for alternative j, often modeled as a linear function of the attributes of the alternatives and their coefficients.

2. Utility Function: The utility for each alternative is modeled as:

where β′ represents the coefficients of the attributes X_ij of the alternatives, and ϵ_ij​ is an independently and identically distributed (iid) error term following a Gumbel distribution.

3. Assumption of IIA: The IIA property implies that the odds ratio between two alternatives is unaffected by the inclusion or exclusion of other alternatives in the choice set. While this makes the MNL model computationally simple and attractive, it may not always be realistic.

4. Applications: The MNL model is commonly used to model consumer choices, such as selecting a mode of transport, choosing a brand, or voting behavior.

​

15

Source:

• McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In Zarembka, P. (Ed.), Frontiers in Econometrics. Academic Press.
• Train, K. E. (2009). Discrete Choice Methods with Simulation. Cambridge University Press.

www.iamo.de/en

Mixed-Logit Model

EAAE 2024| CP3b: Session 3b

• The Mixed-Logit model, also known as the random parameters logit model, is an extension of the traditional multinomial logit (MNL) model. It is widely used in discrete choice analysis, particularly when the assumption of the independence of irrelevant alternatives (IIA) is too restrictive or unrealistic.
• Characteristics:
• Random Coefficients: Unlike the standard logit model, which assumes fixed coefficients, the Mixed-Logit model allows for random variation in coefficients across individuals. This accounts for unobserved heterogeneity in preferences.
• Flexible Substitution Patterns: The Mixed-Logit model can approximate any random utility model, providing much more flexibility in capturing substitution patterns between alternatives compared to the MNL model.
• Utility Function: The utility of a choice in the Mixed-Logit model is given by:

where β_i are individual-specific coefficients (random parameters), X_ij are the observed attributes of the alternatives, and ϵ_ij is the error term.

4. Applications: It is used to model complex decision-making processes where the assumption of homogeneity in preferences does not hold.

​

16

Source:

• Train, K. E. (2009). Discrete Choice Methods with Simulation. Cambridge University Press.
• McFadden, D. (2001). Economic Choices. American Economic Review, 91(3), 351-378.

www.iamo.de/en

Latent Class Analysis (LCA) Model

EAAE 2024| CP3b: Session 3b

• The Latent Class Analysis (LCA) model is a statistical technique used to identify unobserved subgroups (latent classes) within a population based on individuals' responses to observed variables. The model assumes that the population is composed of a finite number of latent classes, each characterized by distinct response patterns.
• Characteristics:

1. Latent Classes: Subgroups within the population that explain variability in observed responses.

2. Class Membership Probability: The probability that an individual belongs to a specific latent class.

3. Conditional Response Probability: The probability of an observed response given membership in a particular latent class.

4. Formula:

The probability of an individual's response pattern x = (x₁, x₂, ..., x_p) across all indicators X₁, X₂, ..., X_p​ is given by:

where:

• π_k is the probability of belonging to latent class k
• P (X_i = x_i ∣ C = k) is the conditional probability of response x_i​ given class k
• K is the number of latent classes

17

Source:

• Lazarsfeld, P. F., & Henry, N. W. (1968). Latent Structure Analysis. Houghton Mifflin.
• McCutcheon, A. L. (1987). Latent Class Analysis. Sage Publications.

www.iamo.de/en

Pilot data analysis

EAAE 2024| CP3b: Session 3b

• Under the multinominal logit (MNL) functional form, the probability for participant n choosing alternative j is:

​

​

​

where is the deterministic component (for participant n and choice alternative j)

are variables (representing the levels l of attribute k)

are model parameters (each parameter​ corresponds to a specific attribute k and indicates how strongly that

attribute influences the choice)

is the random component (the random part of the utility, capturing unobserved factors that affect the participant’s

choice)

18

• We used the Apollo package in R for the computations (Hess & Palma, 2019, 2024)

www.iamo.de/en

Results of the Choice Experiment. MNL

EAAE 2024| CP3b: Session 3b

19

Table 2. MNL model parameter estimates for pilot study data and WTP/WTA

Source: own computations based on pilot study data.

The maximum likelihood estimation on 242 choices made by 43 participants has converged.

www.iamo.de/en

Results of the Choice Experiment. Factors

EAAE 2024| CP3b: Session 3b

​

​

External audit:

• Complete

(WTA 195 €/ha/year)

• Selective

(WTA 77 €/ha/year)

20

The order of attributes with respect to importance:

1) external audit 2) confidence in achieving satisfactory performance

3) additional income 4) type of information to report 5) preparation of the report

Negatively influencing factors

Positively influencing factors

​

Confidence in achieving

satisfactory performance

• Confidence

(WTP 93 €/ha/year)

Preparation of the report

• Support of a professional consultant

(WTP 19 €/ha/year)

Type of information to report

• Water and soil pollution

(WTP 33 €/ha/year)

• Biodiversity

(WTP 18 €/ha/year)

Decision to prepare and publish the environmental performance part of the ESG report for “green” loans

www.iamo.de/en

Pilot survey: answers to the questions.� Which definition most closely matches the concept of “sustainable agriculture”?

EAAE 2024| CP3b: Session 3b

21

Increase profitability, 48%

Reducing pollution (crop rotation, decarbonization, renewable energy, precision agriculture, organic recycling), 44%

Improving social standards (occupational health and safety, training, social protection, gender balance, community support), 31%

Improving management efficiency (risk management, image, certification, anti-corruption, innovation), 33%

I don't know, 2%

www.iamo.de/en

Do you know systems for measuring environmental indicators of sustainable development (Cool Farm Tool, DINAK, RISE, Position Green, etc.)?

EAAE 2024| CP3b: Session 3b

22

I know, 0%

I know something, 3%

I know little, 19%

I know very little, 22%

I know nothing about it, 57%

www.iamo.de/en

What standards, guidelines, recommendations for preparing environmental indicators of an ESG report do you know?

EAAE 2024| CP3b: Session 3b

23

Sustainability Accounting Standards (SASB ISSB)

International Sustainability Reporting Standards (IFRS S1, S2)

Global Reporting Initiative Standards (GRI GSSB)

Integrated Reporting Framework (IIRC)

European Sustainability Reporting Standards (ESRS)

I know

I know something

I know little

I know very little

I know nothing about it

Recommendations of the Task Force on Climate-related Financial Disclosures (TCFD)

Guidance on Carbon Disclosure Project (CDP 2023)

Greenhouse Gas Protocol Corporate Standard (GHG Protocol, Scope 1-3)

www.iamo.de/en

What environmental indicators do you already disclose in your reports and to what extent?

EAAE 2024| CP3b: Session 3b

24

Greenhouse gas emissions

Water and soil pollution

Biodiversity

full disclosure

incomplete disclosure

partial disclosure

minor disclosure

no disclosure

www.iamo.de/en

Does your enterprise currently obtain consulting support to prepare environmental indicators for the ESG report?

EAAE 2024| CP3b: Session 3b

25

Yes, 9%

No, 83%

I don't know, 9%

www.iamo.de/en

Does your enterprise currently undergo an audit of the environmental indicators of the ESG report?

EAAE 2024| CP3b: Session 3b

26

Not conducted, 61%

Selective audit, 14%

Complete audit, 8%

Don't know, 17%

www.iamo.de/en

What measures and areas of investment do you consider to be a priority for the survival of your enterprise during martial law?

EAAE 2024| CP3b: Session 3b

27

Employee reservation, preservation of jobs and salaries, financial assistance to internally displaced persons and the army

Training and retraining of employees to replace those mobilized

Land demining, repair of damaged buildings (structures) and equipment

Construction of reclamation systems and infrastructure reconstruction

very high

high

average

low

very low

Business diversification (provision of services, processing of raw materials)

Fulfillment of contractual obligations (to shareholders, suppliers, customers) and payment of taxes

Strengthening cybersecurity

www.iamo.de/en

What measures and areas of investment do you consider to be a priority for achieving sustainable development goals by your enterprise, regardless of martial law?

EAAE 2024| CP3b: Session 3b

28

Reducing greenhouse gas emissions

Combating land and water pollution

Restoration of biodiversity and ecosystems

Transition to moisture storage practices (cover crops, mulching, afforestation)

very high

high

average

low

very low

Organic and precision farming, minimum and zero tillage technologies

Use of renewable energy (biogas, biodiesel, biomethane, solar power plants)

Transition to bio degraders, circular production (manure, straw)

www.iamo.de/en

What are the risks that hinder investment in sustainable agriculture (assess the significance of each risk)?

EAAE 2024| CP3b: Session 3b

29

Uncontrolled climate change, loss of biodiversity, degradation of ecosystems

Land and water mined and contaminated with explosives as a result of hostilities

Lack of knowledge and experience in environmentally sound practices

Lack of a clear public strategy for environmental practices and financial support

very high

high

average

low

very low

www.iamo.de/en

Assess the readiness of your enterprise to attract international financial resources for sustainable agricultural activities

EAAE 2024| CP3b: Session 3b

30

Completely ready, 6%

Partially ready, 23%

Rather not ready, 49%

Not at all ready, 17%

Don't know, 6%

www.iamo.de/en

How will the launch of the second stage of the agricultural land market (January 1, 2024) affect the implementation of sustainable development practices?

EAAE 2024| CP3b: Session 3b

31

Will have a very positive impact, 3%

It will have a somewhat positive impact, 21%

Will not affect at all, 26%

Somewhat negative, 21%

Very negative, 6%

Don't know, 24%

www.iamo.de/en

Summary

EAAE 2024| CP3b: Session 3b

• Sustainability reporting might be an effective mechanism for attracting private foreign investment in the reconstruction of agricultural entities damaged by the war.
• Public policy in Ukraine should focus on implementing educational programs to train financial consultants in sustainable development and sustainability finance.
• Public programs should support the provision of consulting services by advisory services for small and medium-sized agricultural enterprises.
• Extensive educational activities and events that promote the transition to sustainable agricultural practices should contribute to farmers' awareness of the non-financial benefits of the green transition.
• We predict that in the short term a significantly larger portion of Ukrainian agribusinesses will associate the publication of sustainability reports with benefits other than financial rewards, e.g. improved enterprise image and community well-being.

32

www.iamo.de/en

Acknowledgments�

EAAE 2024| CP3b: Session 3b

This study has received funding through the Marie Sklodowska-Curie Actions for Ukraine (MSCA4Ukraine) program of the European Union.

33

www.iamo.de/en

34

Leibniz Institute of Agricultural Development in Transition Economies (IAMO)

Theodor-Lieser-Str 2

06120 Halle (Saale), Germany

+49 345 2928-0

 iamo@iamo.de

 www.iamo.de/en

​

 iamoLeibniz

 iamoLeibniz

Thank you for your attention!