Navigating Success in Carbon Offset Projects: A Deep Dive into the Determinants Using Topic Modeling

Abstract

Carbon offset projects play a crucial role in tackling the global challenge of climate change. However, there is limited understanding of the factors contributing to the success of a carbon offset project. In this study, we utilize the latent Dirichlet allocation method to extract topics from the descriptions of carbon offset projects sourced from the Gold Standard Foundation. Our findings reveal that projects encompassing both safety and efficient energy solutions for households command higher prices. These results imply that an effective carbon offset project should mitigate individual household emissions while enhancing safety. Our research carries significant implications for stakeholders involved in carbon offset projects and can serve as a foundation for policy formulation and standard regulations.

1. Introduction

Carbon offset projects play an important role in mitigating greenhouse gas emissions and addressing the global challenge of climate change. The projects are designed and executed to reduce carbon dioxide (CO₂) emissions in one area by promoting biodiversity, clean energy, and environmental protection, while compensating for the carbon emission elsewhere [1]. This intricate web of carbon flows constitutes the carbon market, with carbon offset projects fostering environmental and social benefits throughout the whole process.

However, despite the significant advantages of carbon projects, there is an absence of a universal standard to gauge the success of the projects. Currently, four major voluntary offset project registries dominate the landscape, including the American Carbon Registry (ACR), Climate Action Reserve (CAR), Gold Standard, and Verra (VCR). These registries collectively encompass almost all voluntary carbon offset projects worldwide. Additionally, there are over 20 other carbon registries [2], each meticulously tracking projects and issuing offset credits for emission reduction with their own focuses and evaluation criteria.

The success of the projects remains uncertain. The outcome of accreditation could be profoundly affected by platform-specific factors, a knowledge gap that requires resolution. A better understanding of the key factors would be crucial for the projects to ensure the efficacy and realization of their environmental, social, and economic objectives. To the best of our knowledge, existing studies investigating the key influencing factors behind the successful accreditation of carbon offset projects are exceedingly limited. Wu et al. [3] provided a comprehensive overview of carbon neutrality research and relevant practical work, offering a broader and more elevated perspective. Helppi et al. [4] studied the existing guidelines to better understand the influence on companies and societies. von Avenarius’ team [5] did a comparison of the projects on Clean Development Mechanism (CDM) and Verified Carbon Standard (VCS) by using regression analyses to identify the success factors influencing project outcomes. However, none of the studies have explored the critical factors determining project accreditation within carbon registries through the extensive analysis of large datasets. Furthermore, there is a conspicuous lack of clear guidelines on the assessment criteria for companies to refer to during the project initiation and application process.

It is essential to address this knowledge gap to increase the possibility of successful accreditation. Based on this understanding, a comprehensive analysis of certified carbon projects has been conducted to uncover the critical factors that contribute to their success. This study focuses specifically on the Gold Standard, which maintains a detailed and comprehensive database of past projects covering various technologies, methodologies, and execution strategies. Topic analysis has been adopted to effectively analyze the vast volume of data and pinpoint the most influential topics from the project descriptions provided. Therefore, this paper contributes to the carbon offset literature by presenting novel evidence of the determinants of the success of carbon offset projects using a large dataset.

In addition, we have incorporated the theories of systems thinking and life cycle assessment (LCA) into our analysis. Specifically, adopting systems thinking enables the study to perceive the energy value chain as an interconnected and interdependent system. Consequently, our research considers the relationships and feedback loops between different stages, emphasizing the necessity to address issues holistically. The adoption of LCA enables the study to assess the environmental impact from a value chain perspective throughout its entire life cycle, from raw material extraction to end-of-life disposal. Therefore, this study’s innovative theoretical framework contributes to a better understanding of the effects of carbon offsetting projects along the energy value chain.

Lastly, the in-depth analysis of the key topic coverage behind the success of carbon offset projects provides valuable insights to stakeholders involved in the development and implementation of future projects. By doing so, the research is critical to advance sustainable development and contribute to the combat against climate change.

2. Theoretical Framework and Hypothesis Development

2.1. Climate Risk and Carbon Offset Projects

We begin by elucidating the incentives for participants in the voluntary carbon offset (VCO) market, in order to gain a better understanding of the pricing model for carbon credits. The world is transitioning towards a low-carbon and climate-resilient future. For example, various international agreements were established, including the Kyoto Protocol in 1997 and the Paris Climate Agreement (PCA) in 2015, with the objective of curbing global temperature increases to stay below a 2 °C threshold. Besides legal obligations, firms are incentivized to reduce their carbon emissions. First, policymakers have set requirements to encourage investment in sustainable enterprises, especially for sovereign wealth funds and pension plans, which typically have legal limitations on their asset portfolios. Others also join the fight against climate change voluntarily. For example, the coalition Climate Action 100+ was initiated by investors to ensure corporations take necessary action on climate change. According to the US SIF Foundation (January 2019), approximately 38% of financial institutions’ assets under management undergo sustainability screening. Consequently, companies must mitigate their carbon footprint to broaden their ownership breadth.

Second, firms can enjoy a lower cost of capital by improving their carbon performance. Investors may simply aim to advocate for environmentally friendly policies and favor companies with better carbon performance. Baker et al. [6], Flammer [7], Tang and Zhang [8], and Zerbib [9] documented that green bonds, whose proceeds are used for environmentally sensitive purposes, are generally issued with a lower interest rate. Furthermore, investors may demand lower returns for companies with superior carbon performance, as these firms are associated with lower carbon risk. Investors require carbon risk premiums because carbon emissions may be exposed to carbon pricing risk or other regulatory interventions [10]. In the equity markets, investors were documented to demand compensation for firms with worse carbon performance [11,12]. Similarly, because of the difficulty of quantifying the impact of future climate regulation risk [13], firms with better carbon performance are associated with higher tail risk and cost of debt [14,15,16]. In addition, for mortgage credit, the cost is found to be related to climate change risk [17].

Hence, companies have motivations to enhance their carbon performance beyond just meeting legal obligations. Moreover, there are individuals who aspire to reduce their carbon footprint. The demands of such companies and individuals promote the VCO market. In the VCO market, companies and individuals can purchase carbon credits to offset their carbon emissions. Advocates of the VCO market argue that its lower transaction costs make it a more effective option compared to compliance markets [18,19]. As a result, carbon offsetting projects have proliferated to provide carbon credits [20]. Arguably, these finance-related projects play a critical role in climate mitigation [21,22,23]. In particular, Yu et al. [24] showed that an emission trading system has the potential to enhance both firms’ carbon performance and financial performance.

While the nature of carbon offsetting projects implies that their value should primarily be determined by their impact on carbon reduction, it is posited that their value may also be influenced by their effects on other ESG (environmental, social, and governance) dimensions [25]. For example, Gold Standard uses a value-driven model to set a price for carbon credits, because they believe such a model can truly account for the full ESG impacts of a specific project. This may be attributed to the fact, as discussed above, that the demand for carbon offsetting is influenced partly by investors and individuals who are concerned about climate change. These stakeholders are also inclined to value other ESG dimensions [26].

We next proceed to review some important factors contributing to carbon emissions.

2.2. The Role of Household Emissions in Carbon Emissions

Household emissions make a significant contribution to overall carbon emissions. In developed economies like the United States and the United Kingdom, household emissions can account for over 70% of the total emissions [27,28]. While the proportion is smaller in developing countries, rising living standards have led to increased consumer demand, resulting in rapid carbon emissions growth [29,30,31].

Voluminous studies have been conducted to identify the factors for household carbon emissions. Druckman and Jackson [32] and Su et al. [33] found that household income is positively related to household carbon emissions. Weber and Matthews [34], Underwood and Zahran [35], and Ali et al. [36] documented that larger households are associated with higher carbon emissions. Kerkhof et al. [37] showed that higher energy supplies lead to higher carbon emissions. Büchs and Schnepf [38] and Andersson et al. [39] demonstrated that better-educated households tend to have lower carbon emissions.

In particular, improved stoves have been shown to notably decrease household carbon emissions. For example, Zhang et al. [40] conducted a field survey and documented that 85% of rural households are using improper stoves. They also showed that carbon emissions can be significantly reduced by introducing improved stoves. Similarly, Wang et al. [41] proposed a new structure for stoves and estimate that it can improve thermal efficiency and lower carbon emission. In addition to mitigating household carbon emissions, improved stoves can also lower the premature deaths caused by cooking [42].

2.3. The Role of Efficient Electricity Grids and Sustainable Energy in Carbon Emissions

Approximately 24% of carbon emissions are attributed to electricity generation [43]. Therefore, an efficient electricity grid and sustainable energy sources are expected to play a pivotal role in reducing carbon emissions [44].

Concerning the efficiency of the electricity grid, Chen et al. [45] discovered that in China, 80% of inter-regional energy delivery relies on primary coal transport, while 20% is transmitted through secondary electricity channels. They also demonstrated that the efficiency could be significantly enhanced by establishing an inter-regional transmission grid. Varaiya et al. [43] conducted a systematic analysis of the components of a smart electricity grid and concluded that various measures can be implemented to improve its efficiency. Zhou et al. [46] showed that the intensity and energy-mix effects of the electricity grid lead to a reduction in carbon emissions of more than 10%.

Concerning sustainable energy, Fernández et al. [47] documented that in general, R&D on clean energy leads to lower carbon emissions. Similarly, Khan et al. [48] found that policies targeting renewable energy sources significantly reduce carbon emissions. More specifically, Jåstad et al. [49] used data from the Northern European power and heat sector, and demonstrated that the use of woody biomass can reduce the direct carbon emissions by 4–27%. Ravindranath and Balachandra [50] used data in India and found that bioenergy technologies lead to greater carbon emissions reduction. Deprá et al. [51] demonstrated that the effective utilization of a photobioreactor could yield a net energy ratio of 3.44 and achieve CO₂ removal efficiencies of 30.76%. Taborianski and Pacca [52] found that the introduction of a photovoltaic system could potentially reduce carbon emissions significantly. Tilman et al. [53] demonstrated that biofuels provide more usable energy and reduce greenhouse gas emission.

In general, an efficient electricity grid and sustainable energy have great potential to reduce carbon emissions.

2.4. The Pivotal Role of Safety within ESG

Safety constitutes a pivotal aspect of ESG. Refinitiv’s ESG company scores, for instance, encompass three dimensions of safety: public health controversies, customer health and safety concerns, and employee health and safety concerns. Safety is highly valued in the public sector as well. For example, the United States has established the Occupational Safety and Health Administration (OSHA) to enforce safety regulations. Firms also value safety. For example, employee safety is used by American Electric Power as an input in the calculation of executive compensation.

Empirically, there is also evidence supporting the significance of safety within the realm of ESG. Coetzee and VanStaden [54] and Tait [55] demonstrated that major safety incidents attract media attention, hence placing pressure on firms to enhance their safety disclosures. Epstein and Freedman [56] conducted a survey and found that investors demand more disclosure on product safety and quality. Relatedly, Albuquerque et al. [57] showed that firms with better ESG ratings, which incorporate employee safety, are associated with higher valuations.

Since safety holds a pivotal role within ESG, various studies have been conducted to identify the factors affecting safety. Cohn and Wardlaw [58] found that financial constraints lead to higher employee injuries. Caskey and Ozel [59] documented that the pressure from analysts’ forecasts impairs workplace safety. Bernstein and Sheen [60] found that restaurants become safer following private equity buyouts. Similarly, Cohn et al. [61] illustrated that workplace safety aligns with a firm’s long-term value and sees improvement following private equity buyouts. Moreover, Rose [62] showed that the financial health of airlines is positively associated with the safety of airline customers. Shi et al. [63] furnished direct evidence regarding the connection between safety and ESG. They demonstrated that conservative-leaning activist investors, who typically pay less attention to ESG, contribute to poorer workplace safety.

Despite the pivotal role of safety in ESG, the interaction between safety and carbon emission is not straightforward. While there may be technologies that simultaneously improve safety and reduce carbon emissions [42], it is also possible that some carbon emission-reducing technologies are hazardous (e.g., nuclear technology). Furthermore, measures aimed at enhancing safety may inherently require energy and contribute to carbon emissions. For instance, He and Tanaka [64] provided evidence that the energy-saving campaigns implemented after the shutdown of nuclear power plants led to increased mortality rates in Japan.

2.5. Hypothesis Development

Carbon credits are generally priced using a value-driven model that considers the complete ESG impacts of a project. This includes pricing the beyond-carbon ESG benefits. Such a pricing model aligns with the incentives of participating in the VCO market. In developing our hypothesis, we incorporate the theories of systems thinking and life cycle assessment (LCA). Systems thinking views the energy value chain as an interconnected and interdependent system. It considers the relationships and feedback loops between different stages, emphasizing the need to address issues holistically [65]. In our context, the various subjects addressed in VCO projects may exhibit interactions and require systems thinking in evaluation. LCA assesses the environmental impact from a value chain’s perspective throughout its entire life cycle, from raw material extraction to end-of-life disposal [66]. We believe that to evaluate a carbon offsetting project, it is crucial to examine its effect along the life cycle of carbon emissions. As discussed above, both household cooking and energy efficiency (efficient electricity grid and sustainable energy) are important factors in carbon emissions along the life cycle, and great potential exists for improving the two factors. In addition, safety is pivotal within ESG. Therefore, our study focuses on these three topics.

As all three topics are crucial determinants for pricing carbon credits, we do not anticipate any systematic differences in their weighting in the pricing model. Instead, we argue that the interactions between the topics are relevant to pricing, because some projects may generate value on multiple aspects simultaneously. For example, consider two projects. Project A improves the efficiency of the electricity grid, while Project B introduces improved stoves to households in rural areas. While the carbon emission reduction effects may be similar between the two projects, Project B generates additional benefits by reducing indoor air pollution and improving safety. Consequently, Project B will be priced higher.

We hypothesize that projects that involve both efficient energy solutions and safe water and fuel for households are priced higher because such projects are more likely to generate values along the two dimensions at the same time [42]. A potential link could be an improved cooking solution. On one hand, 50–70% of household carbon emissions are from cooking activities [40]. On the other hand, about 3.5 million premature deaths are associated with household solid fuel combustion [42]. Improved cooking solutions can reduce carbon emissions and indoor air pollution simultaneously. However, the interactions are not necessarily positive. For example, it may be difficult for a project to improve household safety as well as electricity grid efficiency and sustainable energy at the same time. For example, some sustainable energy can mitigate carbon emissions but may pose a toxicity risk [50]. It is also challenging for a project to promote an efficient electricity grid and sustainable energy, while reducing carbon emissions from household cooking. Projects on electricity grid efficiency and sustainable energy are generally of large scale, such as setting up a wind power station, and should have little direct impact on household carbon emissions.

Therefore, we have the following hypothesis:

Hypothesis 1.

Carbon offsetting projects that cover the topics of both safe water and fuel for households and efficient energy solutions are priced higher than the projects that do not cover both topics.

3. Methods

To determine how the prominent topical themes in project descriptions affect the Carbon Credits issued, an empirical study was conducted using the topic modeling technique. The study comprised two phases. Phase 1 employed topic modeling in identifying the most prominent topical themes discovered from the project descriptions. In the second phase, the analysis compared the impact of each identified dimension of project topics, and their interaction regarding their effects on the carbon credits issued for the project.

3.1. Data and Topic Modeling

The Gold Standard is one of the most reputable and widely recognized standards for carbon offset projects. Projects registered under the Gold Standard undergo rigorous assessment and verification processes, ensuring that they meet high-quality and transparent standards for emission reductions and sustainable development. A dataset containing 2928 records was collected from the Gold Standard Foundation (GSF) Registry Projects Export. GSF provides a transparent and publicly accessible platform where detailed project information is available. Thus, the GSF Registry serves as a good data source to address our research question. This dataset encompasses crucial information such as Project Name, Project Developer Name, Status, Sustainable Development Goals, Project Type, Country, Estimated Annual Credits, Methodology, Size, Programme of Activities, and POA GSID (Programme of Activity Gold Standard Identity Number).

Of particular significance is the Description field within this dataset, which is subjected to in-depth analysis through Latent Dirichlet Allocation (LDA) [67], which represents one of the most popular natural language processing (NLP) techniques for Topic modelling. LDA is a powerful NLP technique that aims to uncover underlying topics or themes within text data using an unsupervised Bayesian learning algorithm that extracts “topics” from the textual contents on the basis of the co-occurrence of individual terms [67,68]. LDA has several advantages over other existing techniques for extracting dimensions from textual material. First, LDA is highly efficient and can handle large-scale corpora (datasets consisting of textual resources) and sparse matrices. Second, LDA is unsupervised, so researchers do not need to create complex dictionaries. Third, LDA allows for exploratory topic analysis of document collections. By hyperparameter tuning for LDA, researchers can select the optimum number of latent dimensions (topics) and document clusters. Lastly, the latent dimensions extracted by LDA are readily interpretable because there is a direct mapping between each dimension (topic), the keywords, and their weights.

By applying LDA to the project descriptions, it becomes possible to extract meaningful insights and patterns, shedding light on the diverse range of sustainable initiatives recorded in the GSF Registry Projects Export, thereby contributing to a better understanding of global efforts towards sustainable development.

3.2. Text Preparation for Analysis

In preparation for subsequent analysis, the textual data within the project descriptions underwent a thorough cleansing and standardization process. Initially, non-English characters, punctuation, and words containing numerical characters, which typically lack meaningful content related to the topics of interest, were systematically removed. Furthermore, common English stopwords such as “the”, “and”, “when”, “is”, “at”, “which”, “on”, and “in”, which serve primarily as grammatical connectors and do not contribute significantly to connotation or discriminate between topics, were excluded using the predefined list of stopwords provided by the Natural Language Toolkit (NLTK) in Python. The refined dataset resulting from these procedures constitutes the fundamental “corpus” of textual content employed for subsequent analysis, as per the guidelines outlined by previous study [69].

3.3. Dimension Extraction

LDA was implemented using Genism to ascertain the most optimal number of topics [67]. Each topic was then labeled based on the more representative keywords. The model determines the joint probability distribution among the observed words in the project descriptions and the latent topics to be inferred from the distribution of these words. The following sections describe the components of the likelihood specification via the generative process and the dimension inference.

Each document w represents a project description in the corpus D, which includes all project descriptions. A distribution over topics is denoted by Î for each document w. Based on Î, a topic variable $z_n$ is specified for each word in the document w. A k × V matrix β is used to parameterize the word probabilities, where ${\beta }_{ij}=p(w^j=1{|j}^i=1$), which are to be estimated. The LDA model assumes that the topic mixture θ is a k-dimensional Dirichlet random variable, which is built as follows, where the parameter α is a k-vector with Γ(x) representing the gamma function.

$$p\left(\theta |\alpha \right)=\frac{\mathsf{\Gamma }\left({\sum }_{i=1}^k{\alpha }_i\right)}{{\prod }_{i=1}^k\mathsf{\Gamma }\left({\alpha }_i\right)}{\theta }_1^{{\alpha }_1-1}\dots {\theta }_k^{{\alpha }_k-1}$$

(1)

Across the corpus of project descriptions (D), K represents the total number of topic dimensions. In this case, the model assumes that the documents emerge from these latent dimensions, with each document displaying a subset of these hidden dimensions in varying proportions.

The joint distribution of a topic mixture θ, a collection of N topics z, and a collection of N words w is given by:

$$p\left(\theta ,z,w|\alpha ,\beta \right)=p\left(\theta |\alpha \right){\prod }_{n=1}^{N}p\left(z_n\right|\theta \left)p\right(w_n\left|z_n,\beta \right),$$

(2)

where p($z_n$|θ) represents the unique i such that $z_n^i$ = 1. By integrating over θ and summing over z, the marginal distribution of a document is obtained:

$$p(\mathit{w}|\alpha ,\beta )=\int p\left(\theta |\alpha \right)\left({\prod }_{n=1}^N{\sum }_{z_n}p(z_n\left|\theta \right)p(w_n|z_n,\beta )\right)d\theta$$

(3)

Lastly, the probability of a corpus D is obtained by taking the product of the marginal probabilities of all documents:

$$p(\mathit{D}|\alpha ,\beta )={\prod }_{d=1}^M\int p\left({\theta }_d|\alpha \right)\left({\prod }_{n=1}^{N_d}{\sum }_{z_{dn}}p(z_{dn}\left|{\theta }_d\left)p\right(w_{dn}\right|z_{dn},\beta )\right)d{\theta }_d$$

(4)

Both parameters α and β serve as corpus-level parameters that are assumed to be sampled once during the corpus generation process. The variables ${\theta }_d$ represent document-level variables, that are sampled once per document. Finally, the variables $z_{dn}$ and $w_{dn}$ act as word-level variables. Every word-level variable in each document is sampled once.

Once the topics are derived from the corpus, weights on each topic are generated for each document (project description). These weights are then used to predict the Estimated Annual Credits for each project using quantile regression to model the conditional quantiles of the response variable. The model is defined as follows:

$$Q_{\tau }\left(Y|X_1,X_2,X_3\right)={\beta }_0\left(\tau \right)+{\beta }_1\left(\tau \right)X_1+{\beta }_2\left(\tau \right)X_2+{\beta }_3\left(\tau \right)X_3+{\beta }_{12}\left(\tau \right)X_1{X}_2+{\beta }_{13}\left(\tau \right)X_1{X}_3+{\beta }_{23}\left(\tau \right) X_2{X}_3,$$

(5)

where $Q_{\tau }\left(Y|X_1,X_2,X_3\right)$ represents the τ-th conditional quantile of the response variable Y given the values of the independent variables $X_1,X_2,{\mathrm{and} X}_3$;

${\beta }_0\left(\tau \right)$ is the intercept at quantile τ; ${\beta }_1\left(\tau \right), {\beta }_2\left(\tau \right)$ and ${\beta }_3\left(\tau \right)$ are the coefficients associated with the main effects of $X_1,X_2,{\mathrm{and} X}_3$ at quantile τ, respectively; and ${\beta }_{12}\left(\tau \right), {\beta }_{13}\left(\tau \right)$, and ${\beta }_{23}\left(\tau \right)$ are the coefficients associated with the two-way interactions between $X_1 {\mathrm{and} X}_2$, ${ X}_1{ \mathrm{and} X}_3$, and $X_2,{\mathrm{and} X}_3$ at quantile τ, respectively.

By examining different quantiles, we aim to gain a more comprehensive understanding of the nuanced effects of the topical dimensions on distinct segments of the distribution of the response variable. Our selection of 0.25, 0.5, and 0.75 as quantiles aligns with the low, median, and upper extremity of the distribution, respectively. Employing these specific quantiles facilitates a focused analysis at critical points within the distribution, enabling a nuanced examination of the differential impacts that may otherwise remain unobserved in an analysis centered solely on the mean.

4. Results

4.1. Phase 1: Determining the Optimum Number of Dimensions

Selecting the optimal number of dimensions involves a quantitative assessment and the selection of topic models. One common method to evaluate topic extraction is based on topic coherence (C_v) [70]. This metric acts as a concise measure that reflects how likely a topic’s high-probability words are to appear together within the same document, essentially indicating the level of semantic similarity among the top keywords within a topic [71]. The C_v measure relies on a sliding-window one-set segmentation of the most relevant words, along with an indirect confirmation measure that employs a normalized version of the pointwise mutual information (PMI) criterion and the cosine similarity, which is the chosen metric for comparing model performance in this study.

To determine the optimal number of dimensions, we sampled the distribution of coherence scores for various dimension numbers. Figure 1 below illustrates the coherence scores (C_v) as a function of the number of topics, with α = 0.91 and β = 0.91. Upon examining the coherence score plots and their corresponding numbers of topics, it is evident that three dimensions yield relatively high coherence scores. For the purposes of this research, we have chosen to adopt three dimensions over the others, as this framework extracts a set of latent dimensions that are relatively more concise and interpretable.

As illustrated in

Table 1. Latent dimensions and the corresponding top words.

Topics	Representative Top Words and Weights
Topic 1—Improved Water and Energy Usage in Households /Safe Water and Fuel for Households	0.022 × “water” + 0.020 × “households” + 0.011 × “stoves” + 0.011 × “safe” + 0.009 × “activity” + 0.009 × “fuel” + 0.009 × “energy” + 0.008 × “efficient” + 0.008 × “process” + 0.007 × “scale”
Topic 2—Power Generation and Electricity Grid Efficiency/Sustainable Energy	0.016 × “power” + 0.015 × “energy” + 0.014 × “electricity” + 0.012 × “activity” + 0.011 × “grid” + 0.010 × “wind” + 0.009 × “mw” + 0.008 × “emissions” + 0.007 × “capacity” + 0.007 × “households”
Topic 3—Sustainable Cooking Solutions and Biomass Energy/Efficient Cooking Stoves for Households	0.014 × “power” + 0.011 × “cooking” + 0.011 × “activity” + 0.010 × “energy” + 0.010 × “stoves” + 0.010 × “electricity” + 0.010 × “water” + 0.009 × “biomass” + 0.008 × “fuel” + 0.008 × “improved”

, three themes and keywords have emerged from the project descriptions. Qualitative examples of emerging topics were also backed up using manual content analysis, which can be found in Appendix A. The overall findings indicate that project descriptions have primarily focused on the following three aspects: Topic 1—Improved Water and Energy Usage in Households/Safe Water and Fuel for Households; Topic 2—Power Generation and Electricity Grid Efficiency/Sustainable Energy; and Topic 3—Sustainable Cooking Solutions and Biomass Energy/Efficient Cooking Stoves for Households.

4.2. Phase 2: Hypothesis Testing

We first used the Box–Cox transformation to normalize the frequency distribution of the annual credits issued, because it was skewed [72,73]. Box–Cox is a potential best practice when it comes to normalizing data, because it provides a family of transformations that will optimally normalize a specific variable [74,75].

The topic distribution in each project description is expected to affect the estimated annual credits issued for the project. To test our research hypotheses, we formulated quantile regression models to assess both the direct effect and interactive terms, with a focus on estimating the conditional mean of the response variable.

We first looked at the coefficient estimates for the direct effects associated with the topics delineated in the project descriptions. This analysis was performed at conditioned distributions of the response variable at 0.25, 0.5, and 0.75 quantiles and the full sample. Our findings revealed that none of the direct effects were statistically significant (p > 0.05), as illustrated in

Table 2. Parameter Estimates of Direct Effects and Interactions for Quantile 0.5 and 1.0 ^a.

Quantile Regression Results for Quantile 0.25
Parameter	B	Std. Error	Hypothesis Test
			t	Sig.
(Intercept)	14.9963	3.656	4.101	0.000
Topic_1	−3.0385	3.681	−0.825	0.409
Topic_2	−1.5986	3.692	−0.433	0.665
Topic_3	−3.0020	3.682	−0.815	0.415
Topic_1 × Topic_2	−2.4840	0.610	−4.069	0.000
Topic_1 × Topic_3	−0.7439	0.336	−2.213	0.027
Topic_2 × Topic_3	2.2711	0.387	5.869	0.000
Quantile Regression Results for Quantile 0.5
Parameter	B	Std. Error	Hypothesis Test
			t	Sig.
(Intercept)	19.907	4.623	4.306	0.000
Topic_1	−7.9931	4.661	−1.715	0.086
Topic_2	−4.8567	4.665	−1.041	0.298
Topic_3	−5.5422	4.649	−1.192	0.233
Topic_1 × Topic_2	4.6917	0.712	6.593	0.000
Topic_1 × Topic_3	−0.2788	0.416	−0.671	0.502
Topic_2 × Topic_3	−0.4986	0.470	−1.060	0.289
Quantile Regression Results for Quantile 0.75
Parameter	B	Std. Error	Hypothesis Test
			t	Sig.
(Intercept)	4.8435	8.377	0.578	0.563
Topic_1	9.4036	8.456	1.112	0.266
Topic_2	11.7010	8.446	1.385	0.166
Topic_3	9.8874	8.419	1.174	0.240
Topic_1 × Topic_2	3.9955	1.223	3.268	0.001
Topic_1 × Topic_3	2.8901	0.725	3.989	0.000
Topic_2 × Topic_3	1.0732	0.832	1.290	0.197
Quantile Regression Results for the Full Sample
Parameter	B	Std. Error	Hypothesis Test
			t	Sig.
(Intercept)	15.921	8.4712	3.532	0.060
Topic_1	−3.144	8.5410	0.135	0.713
Topic_2	−0.945	8.5486	0.012	0.912
Topic_3	−2.283	8.5197	0.072	0.789
Topic_1 × Topic_2	0.222	1.3039	0.029	0.865
Topic_1 × Topic_3	2.748	0.7615	13.020	0.000
Topic_2 × Topic_3	0.588	0.8619	0.465	0.495

a. Dependent Variable: Transformed Estimated Annual Credits. Model: (Intercept), Topic_1, Topic_2, Topic_3, Topic_1 × Topic_2, Topic_1 × Topic_3, Topic_2 × Topic_3. Maximum likelihood estimate.

. The results align with our systems thinking hypothesis, which conceptualizes the energy value chain as an interconnected and interdependent system. This approach recognizes that interventions and strategies need to consider the broader system dynamics and interdependencies rather than focusing solely on one isolated component.

Thereafter, we tested for two-way interactions among the topics. The results suggest varying characteristics across the various quantiles of the distribution and the full sample.

At the 0.5 quantile, our analysis revealed a noteworthy and statistically significant two-way interaction between Topic 1 (Improved Water and Energy Usage in Households/Safe Water and Fuel for Households) and Topic 2 (Power Generation and Electricity Grid Efficiency/Sustainable Energy) (β = 4.6917, p = 0.000) on estimated annual credits. The finding suggests that an increase in both improved water and energy usage in households and sustainable energy generation and distribution is associated with an increase in estimated annual credits at the 0.5 quantile of the distribution.

Moving to the full sample, our investigation identified another significant two-way interaction, involving Topic 1 (Improved Water and Energy Usage in Households/Safe Water and Fuel for Households) and Topic 3 (Sustainable Cooking Solutions and Biomass Energy/Efficient Cooking Stoves for Households) on estimated annual credits (β = 2.748, p = 0.000). This result signifies that an increase in both Topic 1 and Topic 3 is associated with an increase in estimated annual credits. Both above-mentioned two-way interactions were significant at the 0.75 quantile of the distribution. The significance of this interaction underscores the complex and nuanced relationships between these specific topics and the outcomes measured by estimated annual credits, particularly at the upper end of the distribution.

The empirical findings at both the 0.5 and 0.75 quantiles of the distribution and full sample substantiate lend support to our underlying hypothesis on systems thinking, which posits that projects demonstrating the capacity to generate multiple benefits within the LCA concurrently are more likely to receive higher accreditations.

To better understand the marginal conditions for the full sample, we re-coded Topic 1 by implementing a mid-point split, with 0 representing low mentions of the topic and 1 indicating high mentions of the topic. In instances where mentions of Topic 1 are low, we observed a negative impact of Topic 3 on credits issued (β = −1.301, p = 0.000), as illustrated in

Table 3. Parameter Estimates of Topic 3 on Estimated Annual Credits by Group Coefficients ^a.

Topic_1	Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
Low	(Constant)	14.952	0.089		167.281	0.000
	Topic_3	−1.301	0.137	−0.228	−9.482	0.000
High	(Constant)	12.875	0.074		173.982	0.000
	Topic_3	2.574	0.446	0.168	5.764	0.000

a. Dependent Variable: Transformed Estimated Annual Credits.

. On the other hand, when mentions of Topic 1 are high, Topic 3 exhibited a positive impact on credits issued (β = 2.574, p = 0.000). The empirical results above provide support to our hypothesis, which posits that carbon offsetting projects at the intersection of household emissions and safety topics generate higher carbon credits.

Overall, these results suggest that improved water and energy usage in households, along with sustainable energy solutions, have a positive impact on estimated annual credits at the 0.5 quantile. Similarly, for the full sample, improved water and energy usage in households, along with efficient cooking stoves, have a positive impact on estimated annual credits. Both interaction effects were observed at the 0.75 quantile of the distribution.

5. Discussion

The pressing global concern over climate change and carbon emissions has catalyzed efforts to mitigate the environmental impact of various activities and industries. Among these, household-level emissions play a significant role, and addressing them is crucial in the broader context of reducing carbon footprints. This discussion explores the potential of such initiatives to reduce carbon emissions, as well as the challenges and opportunities they present at scale. The following three pivotal areas will be explored based on the results obtained from the LDA model. These topics present a multifaceted approach to reducing carbon emissions, contributing to a sustainable future.

5.1. Improved Water and Energy Usage in Households and Safe Water and Fuel for Households

The intersection of water and energy usage in households is a critical target for carbon emissions reduction. Inefficient practices in these areas not only lead to resource wastage but also result in unnecessary greenhouse gas emissions. It is essential to understand the symbiotic relationship between water and energy. For instance, studies show that installing solar water heaters in residential areas significantly reduces energy consumption for water heating while conserving water resources [76]. A solar water heater program initiated by the Indian company Nuetech Solar Systems is an example where the use of solar water heaters in homes has led to a substantial reduction in both water and energy consumption [77].

In terms of energy usage in households, promoting the use of energy-efficient appliances has a direct impact on household energy consumption. LED lighting, high-efficiency HVAC systems, and Energy Star-rated devices can significantly reduce energy usage, thereby aligning with carbon offset objectives. These initiatives can be incorporated into carbon offset projects, as the reduction in energy usage translates into lower carbon emissions. The U.S. Environmental Protection Agency’s Energy Star program has led to the adoption of energy-efficient appliances in millions of households, resulting in reduced energy bills and emissions [78].

In the area of water conservation, promoting water-saving techniques such as low-flow faucets, rainwater harvesting, and xeriscaping can reduce water consumption in households. These practices conserve water resources and simultaneously diminish the energy required for water heating and distribution. Many initiatives have been successfully implemented in various regions. Australia’s Water Efficiency Labelling and Standards (WELS) program [79], for example, promotes the use of water-efficient products. By providing consumers with information about water-efficient appliances, this program has led to significant water savings and reduced energy consumption for water treatment and distribution.

Ensuring access to safe water and clean fuel sources is crucial not only for human well-being but also for environmental conservation. In many regions around the world, people still rely on unsafe water sources and traditional solid fuels, leading to detrimental health consequences and an increase in carbon emissions. This is where carbon offset projects play a vital role in addressing these dual challenges. The provision of safe water access to underserved households involves a multifaceted approach that encompasses the implementation of water purification technologies, the development of safe water infrastructure, and the promotion of safe water storage and hygiene practices. Beyond the direct impact on public health, these activities carry an additional benefit: a reduction in the carbon footprint of healthcare systems.

A compelling example of a pioneering initiative in this field is led by UNICEF [80]. This initiative is dedicated to providing safe drinking water to millions of people residing in underserved regions. The impact of such endeavors extends far beyond improved public health; it translates into a significant reduction in the carbon emissions associated with healthcare systems. To illustrate, by diminishing the prevalence of waterborne diseases through the provision of safe water, the UNICEF-led initiative helps reduce the demand for medical treatment and its associated carbon emissions. In essence, this comprehensive approach not only safeguards human health but also contributes to the environmental well-being of our planet by lessening the burden on healthcare systems and their carbon footprint.

Lastly, transitioning households from traditional solid fuels to cleaner and more efficient energy sources is a pivotal component of carbon offset projects. The use of clean cooking technologies, such as improved cookstoves and biogas systems, not only reduces indoor air pollution and associated health issues but also lowers carbon emissions. An example is the sustainable market development of improved cooking in rural Nepal by Value Network Venture Advisory Services. This initiative reduces indoor air pollution and associated health issues, while also significantly lowering carbon emissions. Biogas, generated from organic waste, serves as a cleaner energy source, diminishing the carbon footprint associated with cooking. This shift away from solid fuels enhances the quality of life and safeguards the environment.

At the current stage, scaling carbon offset projects focusing on water and energy efficiency and safe water and fuel access presents several challenges and opportunities. Firstly, encouraging households to adopt energy and water-efficient practices requires effective awareness campaigns and community engagement strategies. The challenge is to inspire lasting behavioral change that aligns with sustainability goals. Secondly, building or upgrading infrastructure for safe water access and clean energy can be complex and costly. Successful projects collaborate with local governments and non-governmental organizations to address infrastructure challenges. Involving local communities in project design and implementation empowers them to take ownership and ensures the sustainability of these initiatives. Therefore, local engagement is a crucial element of success. Finally, it is important to have sufficient policy support provided by the government, as government policies and incentives have a significant impact on the success of carbon offset projects. Advocacy for supportive policies at regional and national levels is fundamental.

5.2. Power Generation and Electricity Grid Efficiency

Power generation and electricity grid efficiency are pivotal areas of carbon offset projects. Traditional energy generation methods are major sources of carbon emissions, and adopting sustainable energy is imperative. Sustainable energy initiatives seek to reduce these emissions by transitioning to cleaner and more efficient power sources and storage solutions.

Renewable Energy Sources: The adoption of renewable energy sources has had a transformative impact on reducing carbon emissions. In India, TVS Energy Limited and MH Technique Solaire India have promoted wind power and solar energy projects, respectively, which generate electricity for the state electricity grid. These initiatives have led to a significant increase in wind and solar power generation, resulting in reduced carbon emissions from the energy sector. Renewable energy sources such as solar, wind, hydro, and geothermal power are not only more sustainable but also more efficient and environmentally friendly.

Energy Storage Solutions: Implementing energy storage solutions is crucial for enhancing the efficiency and reliability of electricity grids. The Tesla Powerwall is a renowned example of residential energy storage solutions that contribute to grid efficiency. These systems allow homeowners to store excess energy from renewable sources and use it when needed, reducing the demand for fossil fuel-generated electricity during peak hours.

Grid Modernization: The modernization of electricity grids, known as “smart grids,” is a powerful strategy to enhance efficiency and integrate renewable energy sources more effectively. The smart grids leverage a network of sensors, meters, software, and communication technologies to change conventional energy management concepts. Spain’s implementation of smart grids has reduced transmission losses and optimized energy distribution. Smart grids enable two-way communication between utilities and consumers, providing real-time data that help reduce energy waste and improve grid stability. Reduced carbon emissions are achieved through optimized distribution and the integration of renewable energy. Enhanced energy efficiency can be achieved by relying on real-time optimization and feedback-based control. The ability to anticipate and respond to disruptions increases grid resilience, minimizing blackouts and ensuring reliable power supply. Finally, by actively participating in the system through information access and demand-side management, end-users become stakeholders and can actively be involved in the eco-system, contributing to the grid’s stability and overall efficiency.

Ultimately, a holistic approach integrating these technologies with improved grid infrastructure, demand-side management, and energy storage solutions is key to achieving cleaner and more efficient energy systems. Continued research and development, coupled with supportive policies and public engagement, will pave the way for a sustainable energy future. To promote sustainable energy initiatives, it is important to understand the distinct challenges and opportunities they currently face. One major challenge is the need for significant upfront investments, which can be mitigated by identifying diverse funding sources, attracting private sector investments, and implementing innovative financing models to address financial barriers. Secondly, the intricate process of developing infrastructure for renewable energy, including wind farms and solar arrays, requires meticulous long-term planning and necessitates collaboration through public–private partnerships. Third, the integration of renewable energy sources into existing electricity grids requires meticulous planning and grid enhancements, which can yield substantial benefits, such as a noticeable increase in residential solar installations and a marked reduction in carbon emissions. Government policies, such as renewable energy mandates and regulations, significantly foster the development and deployment of sustainable energy solutions, contributing to the reduction of carbon emissions. Lastly, ensuring that the advantages of clean energy reach underserved and remote regions is pivotal for achieving broad environmental impact, as it extends the benefits of sustainability to all corners of society.

5.3. Sustainable Cooking Solutions and Biomass Energy

Amid the global effort to mitigate carbon emissions, a significant focus has turned to household cooking practices, underscoring the critical role of sustainable cooking solutions and efficient cooking stoves. Transitioning from traditional biomass fuels to cleaner alternatives represents a cornerstone of carbon offset initiatives in this domain, with a profound impact on environmental conservation and public health [81].

Carbon offset projects in this realm revolve around the adoption of clean and efficient cooking technologies. Improved cookstoves are the vanguards of these initiatives. These stoves are designed to burn fuel more efficiently, emitting fewer pollutants, and reducing the health risks associated with indoor air pollution. The deployment of improved cookstoves not only reduces carbon emissions but also significantly improves the well-being of households. Examples such as the Global Alliance for Clean Cookstoves showcase the immense positive impact of these initiatives, reaching millions of households and resulting in remarkable reductions in carbon footprints [82].

Shifting from traditional biomass fuels to biogas and other cleaner energy sources is a strategic approach to carbon offsetting. Several recent studies, as well as key emerging national and international efforts, are making progress toward enabling wide-scale household adoption of cleaner and more efficient stoves and fuels [42]. Biogas, produced from organic waste, has emerged as a sustainable and low-carbon substitute for household cooking [83]. Initiatives in countries like India exemplify the transformative power of replacing traditional biomass with biogas. This transition not only curtails indoor air pollution but also substantially reduces carbon emissions. These efforts offer a dual benefit by enhancing the quality of life and concurrently curbing carbon footprints.

To maximize the carbon offset potential of efficient cooking stoves, it is essential to promote energy-efficient cooking practices. Behavioral change campaigns and environmental education serve as integral components of these initiatives. Campaigns led by organizations like the Clean Cooking Alliance play a pivotal role in informing households about the environmental and health benefits of clean cooking technologies. By raising awareness and advocating for sustainable cooking practices, these initiatives stimulate lasting behavioral change, aligning communities with carbon offset objectives. Some innovative methods or models to promote efficient cooking stoves in poor areas include financial supports (e.g., targeted microloans) and community-led cooperatives.

While carbon offset projects in sustainable cooking solutions and efficient cooking stoves hold promise, they also confront several challenges and present noteworthy opportunities. One significant challenge is ensuring access to efficient cooking stoves and alternative fuels in underserved regions. The successful adoption of a new technology or idea depends on various factors, including affordability, cultural acceptability, and awareness. Innovative approaches, like microfinance models and community-based distribution networks, are essential to overcome these barriers. These models open doors for broader adoption, fostering carbon offset goals and bringing environmental benefits to marginalized communities.

Promoting clean cooking technologies offers a twofold advantage by reducing carbon emissions and significantly enhancing indoor air quality and public health. Communicating these co-benefits is fundamental for motivating behavioral change and garnering community support. Health and well-being campaigns and initiatives, like those by the Clean Cooking Alliance [82], are pivotal for raising awareness about the health advantages of clean cooking, thereby amplifying the carbon offset impact.

Ensuring a sustainable and reliable supply of alternative fuels, such as biogas or liquefied petroleum gas (LPG), is vital for the long-term success of carbon offset projects. The development of sustainable supply chains is crucial to meeting household fuel demands. These supply chains often involve local entrepreneurship, creating economic opportunities while significantly contributing to carbon emissions reduction.

In summary, the quest to reduce carbon emissions and combat climate change through carbon offset projects is a complex but rewarding endeavor. The three critical areas explored in this discussion offer a multi-faceted approach to carbon emissions reduction. However, the journey towards achieving sustainable carbon offset projects is not without its challenges. Resource allocation, behavioral change, infrastructure development, monitoring and verification, community engagement, and policy support are all crucial elements that must be carefully considered and addressed. By tackling these challenges head-on and seizing opportunities for innovation and collaboration, we can move closer to a more sustainable and carbon-responsible future. Through the collective efforts of governments, organizations, and individuals, it is possible to create a world in which carbon offset initiatives not only mitigate climate change but also improve the lives of millions globally.

6. Conclusions

Carbon offset projects accredited by the global registries are pivotal in the pursuit of sustainable development and climate change mitigation. This research adopted a comprehensive topic analysis of the past projects listed on the Gold Standard registry to unveil the intricate factors that underpin their success.

The findings of this study hold significant value as potential guidelines for project owners. By bolstering the success of project accreditation, this research contributes to sustainable development and climate change mitigation. The results show that the current projects listed on the Gold Standard can be best classified into three major topics: providing safe water and fuel for households, power generation and energy efficiency, and the adoption of biomass cooking stoves for households. The projects covering both household improvement and safety tend to have a higher chance of being accredited among all projects, and thus generate higher carbon credits. This result implies that project safety and equipment reliability are not only primary concerns for project execution, but also key impact factors that affect the beneficiary households and thus the success of project registration. In carbon offset projects, a viable solution should aim to decrease individual households’ emissions while enhancing their safety and property. These findings hold profound meaning for all stakeholders involved in carbon offset projects and may serve as a basis for policy-making and standard regulations.

Despite the critical insights gained from the research, it is essential to acknowledge its inherent limitations:

Generality: Because this research has been conducted based on projects listed on the Gold Standard only, the results may not offer a general understanding of the projects across different platforms. Nevertheless, as an inaugural step, the employed methodology possesses a notable versatility, rendering it amenable to seamless adaptation to diverse platforms. This adaptability facilitates an exploration of the distinctive factors endemic to various registries, thus engendering a heightened comprehension of the disparities in thematic emphasis across dissimilar platforms within the overarching carbon market.

Temporal variability: Success factors for the carbon offset projects are subject to change over time due to evolving technology, shifting market conditions, altered policy requirements, and changing industry standards. When the study period is long, it would be more meaningful to take time into consideration to have a better understanding of the evolution of the critical factors.

Deeper understanding: Although this study provides a general guideline for success factors, it may be necessary to have a more detailed understanding of specific factors for different project types. Such specificity hinges on variables including location, local regulations, culture, and community dynamics. This information will not be available unless we go into individual topics and perform a deeper analysis.

Limited perspective: This research is primarily carried out based on project documentation. There are numerous other factors that may affect the success of the projects, which are difficult to capture in this study, such as geographical and socio-economic contexts and the perspectives of all relevant stakeholders. A more accurate prediction of the registration outcome could only be achieved with all the factors considered. Nevertheless, the current analysis serves as a valuable guideline drawn from the available documents.

While this research provides some valuable insights into the factors historically influencing the carbon offset projects successfully accredited by the Gold Standard, further exploration is imperative in this domain to address the aforementioned limitations and further enhance the understanding of these factors. Here are some future research possibilities:

Horizontal analysis: While some interesting and critical findings can be extracted from the analysis of data from the Gold Standard, a similar methodology could be adapted to other registries as well, to facilitate a horizontal comparison between the keywords across platforms. A more general understanding could be fostered based on the information from various platforms in the voluntary market. In this way, a more common standard could be established for carbon offset projects to serve as a basis for policymaking and market regulation.

Vertical analysis: A deeper analysis could be carried out to drill into each of the topics. With a more specific focus, more factors could be taken into consideration, including project types, scale, locations, and stakeholder perspectives. A more thorough investigation could yield more detailed and specific guidelines regarding the critical factors influencing the success of carbon offset projects.

Longitudinal analysis: Given the dynamic nature of the carbon market and global conditions, conducting a longitudinal analysis over time would be more meaningful to track the evolution of the factors, with a larger dataset collected over a longer period of time. This will be helpful to identify the trend in carbon projects and forecast future direction, highlighting persistent factors crucial for the projects.

Carbon market prediction: A detailed and comprehensive analysis of the historical carbon offset projects can inform predictions on the carbon market trend. Such forecasts provide stakeholders with a clear understanding of future changes in the carbon price, net-zero commitment, driven technology, policies and regulations, and other factors. These predictions would be beneficial for decision-making and policymaking, and would drive the further advancements of sustainability and carbon emission control.

Policy recommendation: This research and potential future studies can be used as a valuable reference for policymaking. They can help to optimize the regulatory environment of the carbon market and establish a common standard for future projects. With a clearer picture and a more systematic understanding of the market, more effective regulations can be implemented to further promote climate change mitigation, benefiting the environment and society at large.

In conclusion, the current carbon offset projects listed on the Gold Standard are important for greenhouse emission control and sustainable development across environmental and social dimensions. The elucidation of critical factors unraveled in the projects holds significant implications for researchers, stakeholders, and policymakers, offering a profound insight into the current carbon market landscape and a well-defined trajectory for future development. This insight would be an important force to propel sustainable development towards a more resilient and impactful direction.