Endogenous Factor Analysis

The Carbon Performance of Public Projects in China

Ziran Tang, Business School, Hunan University, Changsha City, China

Lin Li, Business School, Hunan University, Changsha City, China

Shasha Zhu, Business School, Hunan University, Changsha City, China

Zhenyu Huang, College of Business Administration, Central Michigan University, Mount Pleasant, Michigan, USA

This study investigates how the endogenous factors of public projects affect carbon performance. Taking the logical framework approach, a research model and hypotheses are proposed to evaluate the effects of endogenous project factors, including human resources, funding, materials, and project management methodology on project carbon performance. Questionnaires were distributed to project professionals in China and a structural equation model was deployed to analyze these effects. The results show that funding, materials, and project management methodology have a significant influence on public project carbon performance, whereas human resources have no significant effects. Recommendations on how to improve carbon performance are provided.

KEYWORDS: public projects; carbon performance; endogenous factors; structural equation model (SEM); China


The Chinese central government has chosen to invest heavily in public projects as the primary method for stimulating economic growth (Quéré, 2014). More specifically, provincial and local governments are engaged in the insatiable pursuit of economic growth to increase the gross domestic product (GDP) (Chuan, 2008); in the meantime, they are neglecting the negative effects of this extensive economic growth on the natural environment. In China, carbon emissions in the form of greenhouse gases (GHG) has risen perpendicularly (Olivier, Janssens, & Peters, 2012; Olivier, Janssens, Peters, & Julian, 2011; Liu, 2015). In 2007, China became the largest carbon emissions country in the world (Olivier & Peters, 2010; Watts, 2010); its carbon emissions account for 28% of total global carbon emissions (Clark, 2014) and are greater than the emissions of the United States and the European Union combined (Guan, Peters, Weber, &, Hubacek, 2009; Quéré & Guan, 2014; Liu, 2015). China’s per capita carbon emissions exceeded those of Europe and were 1.45 times the world average level in 2013 (Quéré & Guan, 2014). China’s total carbon emissions will surpass the sum of the United States, Europe, and India in 2019 if they follow current trends (Quéré & Guan, 2014). Therefore, China urgently needs to reduce carbon emissions as it aspires to forge a more sustainable, efficient, innovative, and equal path for the future growth and development specified in the 13th Five Year Plan (Central Committee of the Communist Party of China [CCCPC], 2013).

According to a government report from the State Council of the People’s Republic of China (SCOPRC, 2012), public projects produce the most carbon emissions in China. In Europe, the construction sector, particularly the building sector, consumed more than 40% of the energy (Casals, 2006; Szalay, 2007) and the CO2 emissions were almost 40% to 50% (Fernández & Rodríguez, 2012). In general, the government is responsible for the funding and/or construction and operation of public projects (Hu, Li, & Li, 2012), which refers to a broad category of infrastructure projects, such as public buildings, transport infrastructure, and public services and are usually long-lived physical assets and facilities (Yan, Yin, & Fan, 2004; Floricel & Miller, 2001). These projects have become one of the main driving forces of China’s rapid economic development since the policy reform and opening in 1978 (SCOPRC, 2012; Li, 2013; Council, 2011) and play an important role in China’s economy, providing jobs and attracting foreign direct investment. Public projects, therefore, will continue to play an important role in China’s economic development for the foreseeable future; meanwhile, however, it appears that public projects and carbon emissions control create a paradoxical situation.

Numerous extant studies suggest that China should make its extensive economic development sustainable. They argue that economic development should minimize its impact on the natural environment and suggest that greenhouse gases be reduced (Green & Stern, 2014). A low-carbon economy is a green economy, with low energy consumption and low pollution. This energy revolution relies on energy technology innovation and environmental policy implementation (Levy, 2010; Li, Zhu, & Wang, 2013). Many studies provide approaches for achieving a low-carbon economy. Carbon capture and storage (CCS) is suggested as a technical measure to address energy and environmental demands and limit future carbon emissions. Some believe that carbon capture and storage will promote China’s low-carbon economic development (Liu & Callagher, 2010). Zhang (2010) presented viable paths for the development of a low-carbon Chinese economy through renewable energy and reducing the emission of pollutants. Dietz, Gardner, Gilligan, Stern, and Vandenbergh (2009) believed that household behaviors could make a substantial difference in reducing greenhouse gas emissions. Motoshita. Sakagami, Kudoh, Tahara, and Inaba (2015) contended that information disclosure associated with the carbon dioxide emissions of goods or services may encourage customers to make choices that reduce carbon dioxide emissions; however, these studies offer only micro-level approaches to achieving a low-carbon economy and none of these studies the effects of public works on carbon emissions.

Akanni, Oke, and Akpomiemie (2015) used questionnaires to collect data from government and private developers to analyze how environmental factors affect building project performance by Nigeria’s Delta State. Eriksson and Westerberg (2011) established a testable holistic procurement framework to examine how procurement factors affect project performance, while Odusami, Iyagba, and Omirin (2003) surveyed project leaders in Nigeria to investigate the influence of project leadership and team composition on construction project performance. Sweis, Bisharat, Bisharat, and Sweis (2014) recognized a host of factors affecting this performance and classified them according to Drewin’s open conversion system. These studies have examined how internal and external factors affect public project performance, but they have not researched the relationship between carbon emissions and public projects.

Several studies have attempted to relate corporate carbon performance to financial performance: Hart and Ahuja (1996) found that companies with initially high emission levels could improve their financial performance by reducing carbon emissions. Hoffmann and Busch (2008) defined four indicators—carbon intensity, dependency, exposure, and risk—to measure the transparency of corporate carbon performance and used these indicators to assess companies’ carbon performance. He, Tang, and Wang (2013) found an inverse relationship between carbon disclosure and carbon performance. Yunnus, Elijido-Ten, and Abhayawansa (2014) analyzed the Carbon Disclosure Project (CDP) data on Australia’s top 200 listed corporations to research the relationships between carbon management strategy (CMS) adoption, financial performance, and carbon performance. These researchers found that carbon management strategy adoptions do not have a significant relationship with carbon performance, whereas there is a positive relationship between financial performance and carbon performance.

In summary, there is insufficient understanding of the relationship between carbon emissions and the carbon performance of public projects. Reducing carbon emissions in China is heavily dependent on increasing this performance, affected by endogenous project factors. Hence, this study focuses on discovering the relationship between carbon performance and the endogenous factors of public projects by surveying data and validating appropriate models. This article concludes by providing suggestions to improve the carbon performance of public projects.

The Endogenous Factors of Public Projects and Carbon Performance

Numerous studies have focused on how to evaluate corporate carbon performance (Hoffmann & Busch, 2008; Busch, 2010; Doda, Gennaoli, Gouldson, Grover, & Sullivan, 2016), the relationship between corporate carbon footprints and carbon emissions (Delmas & Nairnbrich, 2011; Goldhammer, Busse, & Busch, 2016), and the carbon supply chain by reducing carbon emissions (Nishitani, Kokubu, & Kajiwara, 2016; Sundarakani, Souza, Goh, Wagner, & Manikandan, 2010; Elhedhli & Merrick, 2012). However, these studies have failed to explain what the carbon performance was or they simply referred to the CO2 emission reduction as the carbon performance. According to the literature (Ozawa-Meida, Brockwaway, Letten, Davies, & Fleming, 2013; Hsu, Kuo, & Chiou, 2014; Wang, Chiu, & Chiu, 2017; Zhou, Ang, & Wang, 2012), we define the carbon performance of public work projects as a new and important element and indicator of overall project performance. Carbon specifically refers to the use of green, recyclable materials and a carbon-reduction project management methodology to reduce carbon emissions during project implementation.

Public projects require input from various production factors (Manning, 2008). The basic production factors include land, labor, capital (Samuelson & Nordhaus, 1995), and entrepreneur-ship (Marshall, 2009; Whitaker, 1988) according to the Distribution Theory of Marshall (2009). The land is used to produce goods or services, including the land space used for the production process and natural resources, such as petroleum, coal, and natural gas. The labor refers to human physical and mental powers that are applied to the production process. Capital consists of the tools that human beings produce for the production of other products (Evans, 2004), and entrepreneurship refers to the management methodologies adopted to organize the production process (Marshall, 2009). Leibenstein (1976) suggested that managerial skills and labor relations are incorporated into production factors in addition to traditional factors. Furthermore, based on a logical framework of project objectives and end-means relationships, the World Bank identified performancemonitoring factors to improve the quality and impact of its work (Mosse & Sontheimer, 1996). These factors showed that project funding (capital), human resources, and materials are endogenous factors that affect project performance. Moreover, another study (Cao & Hoffmann, 2011) contended that the most commonly used project measures include cost and technical performance outcomes (materials). Chan, Scott, and Chan (2004) reviewed previous studies from seven major journals in the construction field and concluded that project management methodology and human-related factors affect project success. These production factors are also required for public projects. When these endogenous factors are applied to the construction process they play major roles in project performance. Carbon performance as part of the overall performance of public projects will also be affected by these production factors, which therefore assumes that these four factors impact the carbon performance of public projects. Then we establish the “Endogenous Project Factors for Carbon Performance” composite index and the carbon performance index to analyze the relationship between the endogenous factors and carbon performance.

Human Resources and Carbon Performance

Human resources are the foundational endogenous factor in the construction process (Belout, 1998; Shahin & Jamshidian, 2006). Nieto-Morote and Ruz-Vila (2011) considered the work experience and professional knowledge embodied in project teams to influence project performance. Another study illustrated that project managers with extensive project management experience, good communication skills, and coordination ability are critical to project success (Lock, 2007; Hwang & Ng, 2013). In addition, Schultz, Slevin, and Pinto (1987) reflected on the selection and training of staff and the capability of project managers as two major factors of project success (Sayles & Chandler, 1992). As a result, we consider that human resources with low-carbon project experience may affect carbon performance; therefore, we propose Hypothesis One (H1):

H1: Human resources with low-carbon project experience will have an effect on carbon performance.

Funding and Carbon Performance

In general, public project construction parties purchase and use conventional energies, materials, and techniques to complete the construction of public projects. However, these conventional production materials generate high levels of greenhouse gases after combustion. In addition, the construction of public projects inevitably affects the natural environment surrounding the project. Semis et al. (2014) demonstrated how financial difficulties directly affect contractor performance in public construction projects, whereas Li, Zhu, and Wang (2013) suggested designating appropriate special funds to purchase green materials and equipment to replace conventional materials and improve project carbon performance, as well as allocating funds to prevent and solve potential environmental issues. Thus, we suggest that the availability of funding for projects to reduce carbon emissions may affect carbon performance, and we therefore propose Hypothesis Two (H2):

H2: Available funds to reduce carbon emissions will have an effect on carbon performance.

Materials and Carbon Performance

Project materials are the resources, technology, and equipment used in the construction of public projects, such as sand, cement, and cranes. Generally, conventional materials are preferred in construction; however, many studies have argued that traditional patterns of resource consumption have resulted in environmental and social problems (Oberthür, Pfahl, & Tänzler, 2005). These studies (World Bank, 2001; Dong, 2009; Abidin, 2010; Sun, Fang, & Chen, 2011) suggest using recyclable and biodegradable green materials for the construction of public projects. Moreover, several studies (Li, Sun, & Guo, 2011; Wang, Chiu, & Chiu, 2017) have found that environmentally friendly technologies, such as wind power generation technology, solar power, and desulfuration techniques for coal, are more efficient than the classical technologies and result in lower carbon emissions. Xu and Lin (2015) argue that human resources and equipment have an impact on corporate carbon performance. Wang et al. (2017) also stated that carbon abatement technologies in Asia have lagged behind levels in developed areas, and Asia has the greatest potential to reduce carbon emission. Stresing, Lindenberger, and Kümmel (2008) analyzed the co-integration of output, capital, labor, and energy in Germany, Japan, and the United States; the result showed that energy factors have the largest effect on the economic weights of production factors. Hence, low-carbon materials may have positive effects on the carbon performance of public projects. Thus, we develop Hypothesis Three (H3):

H3: Low-carbon project materials will have an effect on carbon performance.

Project Management Methodology and Carbon Performance

Project management methodology is defined by Project Management Institute (PMI, 2014) as a set of methods, techniques, procedures, rules, templates, and best practices used on a project (Joslin & Müller, 2014; Špundak, 2014). Project management methodology has been applied in various sectors of industry for more than 30 years (Wierschem & Johnston, 2005). These methodologies offer a scientifically proven, systematic, and disciplined approach to project design, execution, and completion. Chin, Spowage, and Yap (2012) considered that project management methodology could be used to improve the probability of meeting project goals. The Chaos Report (Standish Group, 1994) shows that 31.1% of projects were cancelled before ever being completed, and 52.7% of projects cost 189% of original estimates because these projects did not adopt a reasonable project management methodology. Frame (1997) states there are three main reasons for the failure or cancellation of numerous projects: lack of planning and control of the project, the project cannot perform as expected, and the project’s organization has management problems. Andersen and Jessen (2000) argued that reasonable project planning contributes to project success. Špundak (2014) reviewed different project management approaches, defined project management methodologies, and showed what is considered to be part of a project management methodology in a wider or narrower sense, along with the main characteristics of a methodology. The need for combining project management approaches is shown in the case of a software development project. Shenhar and Dvir (2007) proposed the Diamond Model to classify projects along four dimensions: Novelty (ranging from derivative to breakthrough); Technology (ranging from low-tech to super high-tech); Complexity (ranging from small-scale, simple assemblies to complex arrays); and Pace (ranging from a regular pace to a blitz pace). This model could help managers develop a good sense about how to manage projects. DeCarlo’s (2010) research shows traditional project management methodology is ineffective in new environments, because project construction will encounter instability, rapid change, and heightened complexity in current times. Wang et al. (2017) considered management methodology and gaps are two contributors to the increase in carbon emissions, and the study indicates that management factors are significant overall for carbon emission in different countries. According to the logical framework approach, therefore, successful project implementation affects overall project performance and carbon performance, as part of the overall project performance, will inevitably be affected by project management methodology. Therefore, we propose Hypothesis Four (H4):

H4: Project management methodology will have an effect on carbon performance.

According to the logical framework approach (Rosenberg & Posner, 1979), we know that a project’s endogenous factors and construction process will affect a project’s outputs and impacts. Kueng (1999) stated that business process performance measurements could represent the actual information and performance of the business process. He also noted that the assessment of process performance enables individuals and groups to assess where they stand compared with their competitors. Numerous corporations focus on their business processes to obtain long-term competitive advantages. Kueng (2000) presented a holistic process measurement system to support the business process. Hammer and Champy (2003) proved that redesigning the corporate business process, structure, and culture will improve corporate performance significantly. Similarly, we believe that public project process performance will also reflect practical information about the public project process. This information is extremely important for project stakeholders for the decision making on corrective actions in the subsequent project process (Mosse & Sontheimer, 1996). Consequently, these later period decisions will further influence the outputs and impacts of public projects. Project outputs mean that construction parties apply endogenous project factors to transform project planning into practical results during the construction process (Turner, 2014). Output results can be divided into two categories: tangible and intangible outputs. Tangible outputs are physical forms, such as bridges, power stations, and airports. Intangible outputs are nonphysical forms, such as client satisfaction, end user satisfaction (Serrador & Turner, 2015), and carbon output (Hoffmann & Busch, 2008). Generally, when projects are completed, project stakeholders will inspect the accepted deliverables (Project Management Institute, 2013) and usually use a series of indicators to measure whether project outputs are in line with expectations. Hu, Li, and Li (2012) stated that public project outputs are influenced by the skills of construction personnel, management methodologies, materials, and other factors in the construction process. Hence, we suggest that project output performance can be used to measure project outputs.

According to the analysis described above, we consider that project output performance will be affected by project process performance because public project outputs are based on the construction process. Therefore, we present Hypothesis Five (H5):

H5: Carbon process performance will have an effect on carbon output performance.

Project impact is the project’s influence on the external macro-environment, including its impact on the natural environment, contribution to the economy, and impact on natural resources (Zhai, Xin, & Cheng, 2009), all measured as project impact performance. The influence of a project on the external environment depends on the construction process and the project’s outputs according to the logical framework approach. Therefore, a project’s process and output performance may affect its impact performance. Consequently, we propose Hypothesis Six (H6) and Hypothesis Seven (H7):


Figure 1: The theoretical model of endogenous project factors and carbon performance.

H6: Carbon process performance will have an effect on carbon impact performance.

H7: Carbon output performance will have an effect on carbon impact performance.

The theoretical model of endogenous project factors and carbon performance is depicted in Figure 1.


The logical framework approach was proposed by the U.S. Agency for International Development (Rosenberg & Posner, 1979) as a project design, planning, and evaluation method. Numerous organizations use the logical framework approach as their planning, management, and evaluation procedure for project assessment (Aune, 2000). The logical framework approach is a comprehensive and systematic framework for analysis, focusing on the inherent logical relationships within the project; it includes two aspects, the first of which is the project’s internal logical relationship. This relationship exists throughout the entire project process, from input to output. The second aspect is the causal logical relationship between the project and the external environment (Roduner, Schläppi, & Egli, 2008). Public projects are constructed in natural environments and represent an external intervention that breaks the previous environmental balance. This process creates an interaction between the construction of public projects and the local environment in which this construction and human activities will consume a variety of environmental resources and change the natural environment; simultaneously, the project is impacted by the external natural environment.

Project construction is no longer considered a separate and enclosed event; it is an ongoing interaction with the external environment (Ortengren, 2004). Therefore, this article applies the logical framework approach to analyze the relationships between the carbon performance of public projects and endogenous project factors.

The Survey

To collect data, we conducted a survey. The survey questionnaire consisted of two steps: a pilot questionnaire and a final questionnaire (Converse & Presser, 1986). The initial questionnaire was obtained by reading the literature and consulting experts. We sent the initial questionnaire randomly to respondents and revised the questionnaire based on their feedback. We examined the reliability and validity of the questionnaire and deleted unreliable indicators to form the final questionnaire. The pilot questionnaires were issued in paper and email formats to MBA classes of a Business School of a 985 university (World Education News & Reviews [WNER], 2006; Ministry of Education of the People’s Republic of China [MOEOPRC], 2006) in China. (‘Project 985’ is a higher education system development project in China with the main aim of promoting the development and reputation of Chinese universities. It includes 39 of the best universities in mainland China). These respondents are professionals who work or do research in the public project field in China. Fifty pilot questionnaires were issued, of which 22 were paper and 28 were electronic questionnaires; of the questionnaires returned, 32 were valid. After collecting the pre-test data, we analyzed the mean and correlation coefficients in the population (CITC) to revise the questionnaire and used Cronbach’s alpha to test the reliability of the questionnaire’s scale. We used SPSS 22.0 to analyze the collected data and set the importance evaluation as “generally agreed” (mean >3). The CITC was greater than 0.4 between each question and the other questions in the same variable dimension. Based on these results, we formed the final version of the questionnaire (see the Appendix).


Table 1: Endogenous factors in public project carbon performance.

We asked the respondents to choose which endogenous factors would affect carbon performance (Table 1) in four categories: human resources with low-carbon project experience, availability of funding for projects to reduce carbon emissions, materials, and project management methodology. Table 2 shows the performance index, which influences carbon performance throughout the construction process. In addition, we included some demographic questions in the survey.

The formal questionnaire was issued to people with work experience in China’s public project fields, including project managers, engineers, and civil servants. Select respondents had engaged in public project planning, construction, project evaluations, and daily project management duties and had abundant practical project management experience. Table 3 provides basic information on the respondents; Table 4 lists the types of projects they engage.

Data collection ran for 12 weeks between April and June 2014; a total of 243 questionnaires were issued to respondents in paper and email formats. Specifically, 76 paper versions were issued, and 58 usable responses were obtained, with an effective ratio of 76.32%; 176 email copies were issued and 93 usable responses were obtained, with an effective ratio of 55.69%. In total, we collected 151 validated questionnaires with an effective ratio of 62.2%.


This research applies a structural equation model (SEM) to analyze the relationship between project carbon performance and endogenous factors in public projects. The structural equation model uses a covariance structure analysis to establish the multivariate causal model, which integrates statistical factor analysis and path analysis methods to measure how measured variables affect latent variables (Hatcher & O'Rourke, 2014; Wu, 2013). In this article, the structural equation model is used to validate the causal relationship between variable numbers and evaluate the goodness of fit and the matched degree of this model, considering the following aspects: preliminary fit criteria, overall model fit, and internal structure fit:


Table 2: Project carbon performance scale.

  Category Quantity Proportion
Career People working in the public project field 99 65.56%
  Government department officers 19 12.58%
  Researchers in the energy and materials field 22 14.57%
  Researchers of construction project management 11 7.28%
Educational attainment PhD 10 6.62%
  Postgraduate 38 25.17%
  Undergraduate 94 62.25%
  Other 9 5.96%
Work experience (years) ≤3 23 15.23%
  4-10 68 45.03%
  11-15 30 19.87%
  16-20 15 9.93%
  ≥20 15 9.93%

Table 3: Basic information on respondents.

(1) Preliminary fit criteria: First, the measurement error cannot be negative; second, the measurement error should reach the significance level; third, the factor loading is between 0.5 and 0.95; and, last, the standard errors cannot be too large.

(2) Overall model fit involves three statistical measures: absolute fit, parsimonious fit, and incremental fit. Four indices can be selected for the absolute fit measure: chi square degree of freedom, goodness-of-fit index (GFI), root mean square residual (RMR), and root mean square error of approximation (RMSEA). The selected parsimonious fit measures are the parsimony-adjusted normal fit index (PNFI) and the parsimony goodness-of-fit index (PGFI). The incremental fit measures are the normal fit index (NFI), the Tucker-Lewis index (TLI), and the comparative fit index (CFI) (Table 5).

(3) Internal structure fit includes two measures: the composite reliability and the average variance extracted (Hair, Black, & Babin, 2010). The composite reliability needs to be larger than 0.7, and the average variance extracted needs to be larger than 0.5.

Type Quantity Proportion
Transportation 39 25.83%
Urban infrastructure 23 15.23%
Agriculture and natural resources 14 9.27%
Energy 37 24.50%
Large-scale water conservancy project 12 7.95%
Social undertakings 13 8.61%
Other 13 8.61%
Total 151 100%

Table 4: Project types in the sample.

Therefore, the theoretical model (see Figure 1) can be transformed into the structural equation model path diagram (Figure 2).

As shown in Figure 2, human resources with low-carbon project experience, available funds to reduce carbon emissions, materials, and project management methodology are the exogenous latent variables. Carbon output performance, carbon impact performance, and carbon process performance are the endogenous latent variables.

Analysis and Results

Reliability and Validity

We tested the reliability and validity of each table in the questionnaire, which addresses endogenous project factors, carbon process performance, carbon output performance, and carbon impact performance. First, we standardized the scale for endogenous project factors; then, we obtained the coefficients for the Cronbach’s alpha values of human resources with low-carbon project experience; the availability of funding for projects to reduce carbon emissions; materials; and management methodology, which are 0.736, 0.785, 0.802, and 0.800, respectively. This result indicates good reliability for the endogenous project factors scale. In addition, the model fit is extremely good, as indicated by the confirmatory factor analysis of the endogenous project factors scale: χ2/df = 1.572, GFI = 0.906, RMR = 0.035, RMSEA = 0.060, PNFI = 0.700, PGFI = 0.619, NFI = 0.896, TLI = 0.947, and CFI = 0.959. Additionally, the RMSEA <0.08, and NFI is close to 0.9. These results indicate that the model is acceptable; other indicators also meet acceptable levels (Table 6).


Table 5: Evaluation standard of the degree of fit.

For project carbon process performance, Cronbach’s alpha coefficients of project construction management performance, technology utilization performance, and resource utilization performance are 0.878, 0.852, and 0.743, respectively, indicating good reliability for the project process performance scale. The model fit for the carbon process performance confirmatory factor analysis (CFA) is also acceptable. For carbon output performance, Cronbach’s alpha coefficients of project financial evaluation, quality management evaluation, environmental benefit evaluation, and stakeholder satisfaction ratings are 0.774, 0.316, 0.848, and 0.597, respectively. We deleted the quality management evaluation subscale because the Cronbach’s alpha coefficient of the quality management evaluation is below the evaluation standard. The Cronbach’s alpha coefficients of the other subscales are higher after they have been normalized; therefore, the reliability of the project output performance scale is good and the validity analysis also passes the test. The Cronbach’s alpha coefficient of economic and social influences for project carbon impact performance is 0.823 and that of environmental influences is 0.886; therefore, the reliability of the project impact performance scale is good. The model fit of the carbon impact performance confirmatory analysis is good, and the fit index reached an acceptable level.


Figure 2: The structural equation model path diagram and its parameters.

Hypothesis Testing

We verify the theoretical model (see Figure 1) using AMOS17.0. More specifically, human resources with low-carbon project experience; the availability of funding for projects to reduce carbon emissions, materials, and project management methodology are exogenous latent variables; and project process performance is an endogenous latent variable used for calculation. The absolute fit measures, parsimonious fit measures, and incremental fit measures are as follows: χ2/df = 1.869, GFI = 0.852, RMR = 0.054, RMSEA = 0.073, PNFI = 0.667, PGFI = 0.646, NFI = 0.801, TLI = 0.873 and CFI = 0.894. The RMSEA < 0.08, and the NFI is close to 0.9, indicating that the GFI as indicated by these three types of measures for the entire model reaches the ideal value or close to ideal value; therefore, the overall goodness of fit for the model and data is good (Table 7).


Table 6: Endogenous project factors system: Fit index of the confirmatory factor analysis (CFA).

In the initial model, the standardized path coefficient of the availability of funding for projects to reduce carbon emissions in project carbon process performance is 0.331, and the t-value is 3.072. The standardized path coefficient of the project material inputs in carbon process performance is 0.606, and the t-value is 3.894. The standardized path coefficient of the project management methodology in project carbon process performance is 0.210, and the t-value is 1.964. These results mean that they reached the significance level. The standardized path coefficient of human resources with low-carbon project experience inputs for process performance is –0.123, and the t-value is –1.178; therefore, it does not meet the significance level of 0.05 and does not pass the test (Table 8).

Our hypotheses are supported except for Hypothesis One (H1); the model path diagram is presented in Figure 3.

In addition, we establish that the structural equation model for project process performance affects project output performance and impact performance. In this model, project process performance is the exogenous latent variable, and project output performance and impact performance are endogenous latent variables for calculation. The GFI for the overall model—including absolute fit measures, parsimonious fit measures, and incremental fit measures—is shown in Table 9. These fit indices meet or are close to the ideal value and the overall goodness of fit of the model is comparatively ideal.

Hypotheses H5, H6, and H7 were tested using AMOS17, and the results are shown in Table 8; all hypotheses are supported according to the results shown in Table 10.

The standardized path coefficient of project process performance on project output performance is 0.640, that of project process performance on project impact performance is 0.263, and that of project output performance on project impact performance is 0.404. All these results are significant. In addition, the P value for project process performance on project impact performance is 0.100 due to complexity. We accept these hypotheses within a 90% acceptable range; the model path diagram is presented in Figure 4.

We checked the fit of two aspects: project inputs with project process performance and project process with project output performance and impact performance. Then, we removed the non-significant paths and used AMOS 17.0 to re-verify the model fit. The modified structural equation model is shown in Figure 5.


For the analysis mentioned above, the standardized path coefficient of the impact of human resources with low-carbon project experience input on carbon process performance is –0.123, and the t-value is –1.178. This does not meet the significance level of 0.05; therefore, Hypothesis One (H1) is not supported.


Table 7: Modified model fit results.

The results show that low-carbon materials have the greatest impact on carbon process performance, output performance, and impact performance, as the influence values are 0.592, 0.461, and 0.331, respectively. Renewable energy, environmental protection equipment, energy-saving materials, and energy-saving technology are the four highest-ranking, low-carbon material inputs in the classification index.

Research Hypothesis Standardized Parameter Estimates t-Value Results
H1: Human resources with low-carbon project experience have positive and significant effects on carbon performance in public projects. –0.123 –1.178 No support (–0.123 < 0.05)
H2: Availability of funding for projects to reduce carbon emissions has positive and significant effects on carbon performance in public projects. 0.331 3.072 Support (0.331 > 0.05)
H3: Low-carbon materials have positive and significant effects on carbon performance in public projects. 0.606 3.894 Support (0.606 > 0.05)
H4: Low-carbon management methodology has positive and significant effects on carbon performance in public projects. 0.210 1.964 Support (0.210 > 0.05)

Table 8: Hypothesis test results for the influence of endogenous project factors on carbon performance.

Available funds to reduce carbon emissions have a positive impact on carbon process performance, output performance, and impact performance, and the influence values are 0.301, 0.234, and 0.168, respectively. Low-carbon funding commitments, project environmental protection funding, and amount per unit land capital rank fourth, fifth, and sixth, respectively, in all classification indexes in terms of their impact on carbon performance.

Project management methodologies have positive impacts on carbon process performance, output performance, and impact performance, as their influence values are 0.179, 0.139, and 0.10, respectively. Low-carbon management methodologies involve five indicators: low-carbon construction plans, resource utilization plans, public feedback plans, risk control, pollution control plans, and garbage recycling programs, which affect carbon output performance with values of 0.102, 0.083, 0.089, 0.088, and 0.084, respectively. These indicators also affect project carbon output performance, with influence values of 0.073, 0.060, 0.064, 0.063, and 0.061, respectively. According to the AMOS 17.0 criteria, project management methodologies have played a full mediating role in carbon output performance and impact performance. Alterations of the project management methodology will increase or decrease the total level of project carbon performance.

In summary, the relationship among these variables is complex. This study summarizes the direct and indirect effects among each variable. We sort the variables by their influence on carbon performance (Table 11).


Figure 3: Model pathway for how endogenous project factors affect carbon process performance.


In this article, we investigated the endogenous factors of public projects that affect carbon performance. The results indicate that the use of low-carbon materials has the greatest influence on carbon performance, followed by available funds to reduce carbon emissions, and project management methodology. Human resources with low-carbon project experience have no influence on carbon performance; therefore, we make several suggestions for improving the carbon performance of public projects according to these research results and the Chinese context.


Table 9: The model fit results for project process performance affect project outcome performance and impact performance.


First, parties in construction projects should adopt low-carbon energy and environmental protection techniques to replace traditional fossil-fuel energy and technology throughout the construction process. This means that project purchasing officers should procure more alternative energy resources, new energy technologies and environmental protection equipment, and fewer traditional materials and equipment, depending on practical concerns during the materials procurement process. In addition, China’s government should set new energy policies and laws, such as tax breaks, government subsidies; low interest, or interest-free bank loans, to encourage project stakeholders; and contractors to use renewable energy technologies and carbon dioxide purification equipment.

Second, project stakeholders should allocate funds to enhance the project’s carbon performance. Public project plans should state clearly how much capital will be used to purchase low-carbon materials and improve carbon performance; this availability of funding for projects to reduce carbon emissions is guaranteed to improve carbon performance. These special funds are used to purchase low-carbon materials and pay higher salaries to staff with low-carbon project management experience, as well as employees with low-carbon knowledge training. In addition, we recommend establishing external governance to monitor the availability of funding for projects to reduce carbon emissions to ensure that these funds are not misused, which means that each funding transaction should be recorded to avoid wasting special funds. Moreover, government departments should review public project contracts and project funding plans before beginning the project. This review will help to prevent project stakeholders from making fraudulent funding plans in order to win project rights, as it will follow up on and keep accountable a project’s availability of funding for projects to reduce carbon emissions.

Third, we suggest developing and adjusting public project management methodologies according to practical considerations regarding the project. Generally, public projects are broad cross-regional projects characterized by large investments and long construction times. The negative impact of these projects on the environment is self-evident; conventional project management methods, however, focus on how to complete a project within the time and at the quality level of the project plan. We suggest, therefore, that project stakeholders should update previous project management methodologies and formulate new environmental protection plans in the project’s daily operations and management programs, such as a carbon-rich gas management plan, an energy conservation plan, and a reforestation program. Furthermore, the project management team and stakeholders need to cooperate with local environmental administration departments to discover potential environmental issues, caused by project construction and develop an environmental contingency plan in advance.

Finally, the research results demonstrate that human resources with low-carbon project experience have no impact on project carbon performance. We still consider that human resources probably play a role in project performance to some extent, because project approvals, project establishment, project bidding, and construction are completed by human resources. In addition, project performance is affected by human resource inputs through the logical relationship between project inputs and outputs, according to the logical framework approach. Schultz (1961) and Becker (1962) stated that human resources are “live” capital and display active innovativeness and creativeness by allocating resources efficiently and adjusting the enterprise development strategy to meet market demands. Furthermore, carbon performance is part of project performance and, in theory, will also be affected by human resources with low-carbon project experience. Therefore, we also proposed improving project carbon performance in relation to this factor. Project stakeholders should establish a professional management team with low-carbon project management and operating experience. This team could instill low-carbon awareness throughout the entire project plan and construction process to improve carbon performance. Furthermore, one study considered that when project transactions costs are reduced, project stakeholders offer a higher level of trust to the project manager (Müller, Turner, Andersen, Shao, & Kvalnes, 2014). Consequently, we suggest that stakeholders should carefully select the project manager and should not excessively intervene in his or her management. In addition, stakeholders need to organize staff to conduct low-carbon knowledge training and help them understand what the low-carbon economy is and how to operate low-carbon technologies and equipment because employees are at the forefront of the entire project construction process.

Research Hypothesis Standardized Parameter Estimates t-Value Results
H5: Project carbon process performance has a positive and significant influence on project carbon output performance. 0.640 5.478 Support
H6: Project carbon process performance has a positive and significant influence on project carbon impact performance. 0.263 1.643 Support
H7: Project carbon process performance has a positive and significant influence on project carbon impact performance. 0.404 2.562 Support

Table 10: Hypothesis test results showing how project process performance affects the project outcome performance and impact performance.


Figure 4: Influence of project process performance on project output performance and impact performance.


Figure 5: The model diagram of the full pathname (modified).


Table 11: The impact of endogenous factors on carbon performance (direct effect, indirect effect, total effect, and ranking)

Academic and Practical Implications

The contributions of this study are as follows: First, we provide a definition of the carbon performance of public projects and suggest ways to measure it, whereas previous research in the field has failed to do so. Most studies have focused on corporate carbon performance assessment (Hoffmann & Busch, 2008; Busch, 2010; Doda, Gennaoli, Gouldson, Grover, & Sullivan, 2016), carbon footprint (Delmas & Nairnbrich, 2011; Goldhammer, Busse, & Busch, 2016), carbon supply chain management (Nishitani, Kokubu, & Kajiwara, 2016; Elhedhli & Merrick, 2012), and carbon emission with climate change (Nordhaus, 2007). Our research provides a valid reference for future studies on the carbon performance of public projects. Second, the research results indicate that materials have a significant influence on the carbon performance of public projects, whereas the impact of human resources on the carbon performance is insignificant. Third, we have established a measurement system for the carbon performance of public projects, which can be used by future studies to measure carbon performance in China and other countries and also provides a basis for future adjustment and improvement on carbon performance measurement. Fourth, our research found that the effect of project management methodology on the carbon performance of public projects deserves further study. The extant literature is interested in the use of low-carbon technology to reduce carbon emissions. The effects of project management methodology on the carbon performance of public projects are widely ignored. However, project management methodology runs through the entire project life cycle and links project inputs, processes, outputs, and impacts all together. Only via a good project management methodology can the effects of materials, human resources, and funds on the carbon performance of public projects be realized and maximized. More research should investigate the impact of project management methodology on the carbon performance of public projects in the future.

This research provides a comprehensive instrument to assess the carbon performance of public projects in China. This instrument can be employed by project stakeholders and third-party inspection agencies to measure carbon performance from project to project. Furthermore, the results of this research can be used as a reference for the Chinese government to develop new environmental policies and laws for public projects. The current policy (Congress, S. C. O. P., 2000; People, C. N., & Congress, S., 1989) does not define the carbon performance of public projects, because past project inspection did not have to consider carbon performance. The policy and lawmakers in China should take into account the effects of all project elements on project carbon performance. In addition, the regulatory authorities, such as the Ministry of Environmental Protection, National Development and Reform Commission, and State-owned Assets Supervision and Administration Commission should review the public project applications to ensure low-carbon endogenous factors are used for public work projects. The research results demonstrate that materials, funds, and methodology have positive impacts on carbon performance. Consequently, there should be an emphasis on these endogenous factors from the beginning of public projects, when carbon performance is one of the goals of public projects sponsored by governments.


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Ziran Tang is a doctoral candidate in Project Management in the Business School of Hunan University, Changsha City, China. He holds a Hons. BA degree in Business Studies from the University of Greenwich, London, England and an MSc degree in Finance and Management from the University of Keele, Stoke-on-Trent, United Kingdom. His research interests include project risk management, public private partnership (PPP), financial time series analysis, and collaborative innovation. He can be contacted at jordantang@hnu.edu.cn

Lin Li, PhD, Professor in Management, PhD Business Information and E-commerce Department Business School, Hunan University, China. He received a BS degree in Mathematics in 1985 from Hunan Normal University, Changsha City, China; an MSc degree in Management Science and Engineering in 1994 from Hunan University, Changsha City, China; and a PhD in Management in 2001 from Hunan University. He was a visiting scholar in the Fisher Business School of Ohio State University, Columbus, Ohio, USA, from 2009 to 2010 and is currently a professor in the Business School of Hunan University. Professor Li has published in numerous management journals in China and his research interests include information system management, ecommerce, financial econometric, performance evaluation, project management, low-carbon economy, collaborative innovation, and public projects. He is the corresponding author in this article and can be contacted at li2518@hnu.edu.cn. This research is supported by the Natural Science Foundation of China No. 71473076.

Shasha Zhu is a postgraduate student in Project Management in the Business School of Hunan University, China and she holds an MA degree in Management from Hunan University, Changsha City, China. Ms. Zhu’s research interests include project management, corporate finance, and collaborative innovation. She can be contacted at rintilor@126.com

Zhenyu Huang, Professor in Information Systems, PhD, Business Information Systems Department, College of Business Administration Central Michigan University, USA. He received a BA degree in Computer Science in 1993, an ME degree in Industrial Management in 1996, and PhD in Management Information Systems (MIS) in 2003. He worked at PriceWaterhouseCoopers as an IT service consultant and is currently a professor in Central Michigan University, Mount Pleasant, Michigan, USA. He has published in high-level MIS journals and presented at leading MIS conferences. Professor Huang’s research interests include enterprise resource planning (ERP) systems, knowledge management, business intelligence, big data and data analytics, healthcare information systems, inter-organizational information systems, electronic commerce, e-government, data visualization, software design and development, system usability, and gamification. He can be contacted at huang1z@cmich.edu

Appendix: Questionnaire

Questionnaire Number:

Survey: Endogenous Factor Analysis: The Carbon Performance of Public Projects in China

Dear Sir/Madam:

Thank you for supporting our project research survey.

This survey is a part of Humanities and Social Science Research Fund Project No. 10YJA630079 of the Ministry of Education of the People’s Republic of China and No. 71473076 of the Natural Science Foundation of China. The objective of this survey is to discover the connection between project endogenous factors and carbon performance in China. Your answers will provide great assistance in our project research and also contribute to improving public project management and carbon performance in China.

In this survey, you need to reply to all questions according to your work experience and the specific conditions of a project and we promise that the contents of the questionnaire will be kept strictly confidential and are intended for academic research analysis. Questionnaires will be processed in accordance with strict statistical procedure and will not involve a specific unit or individual.

Thank you for your support and cooperation.

Project Research Group

We will describe the concepts of public project, low-carbon economy, and carbon performance to help you complete this survey as follows:

Public Project: In general, public projects are government investment projects conducted to meet the demands of the public. They include infrastructure projects and social welfare programs, including airports, roads, nursing homes, and parks. These projects have some common features: large investment, long construction period, non-profit, or low profitability.

Low-Carbon Economy: The low-carbon economy is a green economic development model, based on low-energy consumption, low emissions, and low pollution. Its goal is to promote and apply green energy and low-carbon technology to replace traditional fossil energy, technology, and establish a low-carbon industry system in the economic development process. The aim of low economy is to improve energy efficiency, reduce carbon emissions, and further economic sustainable development.

Carbon Performance: Carbon performance is a part of project performance and is based on low-carbonization project input, including low-carbonization human resources, low-carbonization funding, low-carbonization materials, and low-carbonization management methodology. Project constructors apply green energy, energy-saving technology, and efficient management methods to achieve carbon performance during the project construction process.

Part One:

Table One: Project Carbon Performance Endogenous Factors

Carbon performance is affected by low-carbonization endogenous factors. Therefore, we consider increasing low-carbonization inputs in the endogenous factors. Please consider carefully which factors have an influence on carbon performance and fill in this part. Use a ‘√’ to choose your answer.

(Note: There are five options: 1, 2, 3, 4, and 5. The 5 means this factor has the greatest impact on carbon performance; 4 means this factor has greater impact on carbon performance; 3 means this factor has general impact on carbon performance; 2 means this factor has lower impact on carbon performance; and 1 means this factor has the lowest impact on carbon performance.)


Part Two:

Table Two: Project Carbon Performance Scale

Project carbon performance includes project process performance, output performance, and impact performance.


Part Three: Basic Information

1. Your occupation:

( ) Employee in public projects management ( ) Civil service in public department
( ) Academic researchers ( ) Other

2. Your educational background:

( )  Undergraduate    ( )  Postgraduate    ( )  PhD    ( )  Other

3. How long have you been working in the project management research or project practical work?

( )  ≤3 years    ( )  4–10 years    ( )  11–15 years    ( )  16–20 years    ( )  ≥20 years

4. The project type:

( )    Traffic (road, railway, city transportation, and water transportation)

( )    Urban infrastructure (urban water supply, sewage treatment, refuse disposal, heat supply, treatment of water environment, cultural heritage conservation, and city services)

( )    Agricultural and natural resources (irrigation drainage and flood control)

( )    Energy (traditional energy, hydroelectric, renewable energy, and tunnel)

( )    Large-scale water conservancy project (reservoir)

( )    Social work project (school, hospital, and public park)

( )    Other

This is the end of the survey. Thank you again for your support.

Project Management Journal, Vol. 48, No. 5, 25–48
© 2017 by the Project Management Institute
Published online at www.pmi.org/PMJ

This material has been reproduced with the permission of the copyright owner. Unauthorized reproduction of this material is strictly prohibited. For permission to reproduce this material, please contact PMI.



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