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The US Environmental Protection Agency (EPA) has announced the recipients of a $7 billion grant initiative aimed at increasing solar energy in low-income and disadvantaged communities. The initiative is part of the $27 billion Greenhouse Gas Reduction Fund, part of the Investing in America program funded through President Biden’s Inflation Reduction Act.

A total of 60 select individuals, including states, territories, tribal governments, municipalities and nonprofit organizations, will benefit from the Solar for All program. The initiative is designed to fund the development of sustainable residential solar projects targeting more than 900,000 households across the country. The primary goal is to reduce energy costs, create quality jobs in underserved areas, and promote environmental justice while addressing the pressing issue of climate change.

EPA Administrator Michael S. Regan emphasized the administration’s commitment to ensuring that no community is left behind, with the investment expected to create hundreds of thousands of jobs and save families nearly $8 billion in energy costs. In addition, the projects are predicted to make a significant contribution to improving air quality and combating climate change.

John Podesta, Senior Advisor to the President on International Climate Policy, echoed these sentiments, citing solar power as the most cost-effective form of electricity currently available. He noted that the Solar for All grants will extend the financial benefits of solar power to economically disadvantaged groups that have historically been marginalized in terms of energy efficiency.

Additionally, US Department of Housing and Urban Development (HUD) Acting Secretary Adrianne Todman noted that the grants will improve energy efficiency and climate resilience in affordable housing across the US. , thereby promoting healthier community environments.

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According to an analysis of 26 projects, EU member states have dramatically increased their solar energy goals within their updated national energy and climate plans.

SolarPower Europe said that 26 of the 27 EU countries have submitted updated national energy and climate plans (NECPs), increasing their solar targets by an average of 87% from previous versions.

Governments are banking on renewables more than ever, but grid and flexibility planning lags far behind renewable targets, putting the energy transition at risk, the organization added.

EU member states were supposed to submit updated draft NECPs last summer. The only country missing is Austria. SolarPower Europe noted that its NECP is “on hold indefinitely”. In 2019, the European Commission approved the previous phase of the plans in 2019.

According to the analysis, Lithuania and Ireland stand out by increasing their targets by more than five and 10 respectively. Poland tripled its target, while Finland, Portugal, Slovenia and Sweden more than doubled their previous targets. Spain has increased its ambition by 95%, the report says.

NECPs lack long-term support schemes, targets for consumers

The Union stressed that most NECPs fail to provide long-term visibility to 2030 by introducing support schemes. Besides auction pipelines, investors also need a clear view of self-consumption schemes, especially as net metering is being phased out, he added.

Regarding decentralized photovoltaic generation, only Spain, France, Croatia, Ireland, Portugal and Romania have provided quantitative targets for rooftop PV. The rest do not distinguish between rooftop and utility-scale facilities, which creates a problem for the visibility of the rooftop market, the report’s authors emphasize.

The description and trajectory of support earmarked for manufacturers or energy communities is often limited or partial, he said. Only Greece, France, Ireland, Italy and Lithuania provided sufficiently detailed information on consumers, while Germany, Greece, Ireland, Italy and Poland passed the energy communities segment, the document said.

Bonadio: Without proper system planning, solar projects will go to waste

SolarPower Europe Senior Policy Advisor Jonathan Bonadio said Europe risks putting the cart before the horse. “Energy system planning should be aligned with energy production targets. “Without proper energy system planning, solar projects will not be implemented, solar energy will be wasted and the business case for solar energy will be undermined.”

According to the report, NECPs fail to truly connect the dots that are critical to making the energy transition a reality: grid deployment, modernization and flexibility. Less than half of the EU countries have relevant energy storage targets, only two plan relevant investment in their distribution networks, and only four have provided a realistic target for demand-side flexibility through the roll-out of smart meters or demand-side response: Belgium, Bulgaria, Cyprus and Croatia.

Only two countries have set a target or investment plan for their distribution network

This demand-side gap is preventing citizens from adapting to the new energy reality, the report says.

Its authors emphasized demand-side flexibility as a key tool to reduce investments in slow-to-build network infrastructure.

Regarding energy storage, plans for Belgium, Bulgaria, Cyprus, Greece, Spain, Croatia, Hungary, Lithuania and Portugal include specific targets such as megawatts, megawatt-hours or euros.

The analysis shows that while the lack of storage infrastructure or demand-side response undoubtedly and unnecessarily puts pressure on the electricity grid, only France and Malta have set a target or investment plan for their distribution electricity grid.

SolarPower highlighted that the overall ambition for 2030 is seen at 626 GW, compared to the EU Solar Strategy target of 750 GW and industrial capacity of 902 GW.

Governments now have until June 30 to submit any updates before their plans are considered final.

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This section begins by presenting the Conceptual framework, which identifies the explanatory and latent variables that influence the adoption of PV and/or EV. Subsequently, the Modelling approach, Data, and Sample description are described.

Conceptual framework

In this paper, consumer choice to adopt PV and/or EV is explained using the exogenous explanatory variables and endogenous latent variables presented in the conceptual framework in Fig. 1. The choice is presented as a nominal dependent variable with four categories: (1) only adopting PV, (2) only adopting EV, (3) adopting both PV and EV, and (4) adopting none.

Figure 1

Based on the literature review, a set of socio-demographic and two latent variables representing the environmental attitude and technology attitude were used as determinants of the main outcome variable (PV or/and EV adoption). Sociodemographic characteristics are divided into individual-level (gender, age, and employment status), household-level (income, household size [continuous measure of the total number of individuals within each household], dwelling type, dwelling ownership, current change in work location, and residential location), and household energy and appliances (ownership of smart meter, energy management system, and electricity intensive appliances, such as a pool or multiple refrigerators) variables.

Constructs representing attitudes towards the environment and technology were extracted based on a factor analysis of eight attitudinal items elicited in the survey using 5-level Likert scales. The first latent variable, pro-environmental attitude, is used to capture how important climate change is to the consumer and their belief of having the power to change this issue by acting differently. This latent variable is relevant because it reflects the growing awareness and concern about environmental sustainability among individuals. As the effects of climate change become more apparent, consumers are increasingly recognising the need to adopt greener technologies and practices to mitigate its impact. Those with a stronger pro-environmental attitude are more likely to prioritise sustainable choices and seek out environmentally friendly alternatives. The second latent variable represents an individual’s interest in technology, specifically related to the adoption of new technologies and use of smart energy management. This latent variable is relevant because both PV and EV are emerging technologies, and it is expected that individuals with a positive technology attitude are more likely to embrace new technologies and recognize their potential benefits, making them more inclined to adopt PVs and EVs. By incorporating these latent variables into the analysis, we can examine the influence of environmental and technology attitudes on PV and EV adoption. Gaining insights into the underlying psychosocial factors that drive consumer behaviour enables policymakers and other stakeholders to design targeted interventions and strategies that encourage the adoption of sustainable technologies.

Modelling approach

Considering the nominal discrete nature of the outcome and the latent constructs, we utilise an integrated choice and latent variable (ICLV) modelling approach. This model incorporates the effects of latent variables on choice behaviour, allowing researchers to explore how unobserved factors, such as attitudes or preferences, influence individuals’ decision-making processes. The ICLV model consists of two components: a discrete choice model and a latent variable model11. The components of the modelling framework are presented in Fig. 1. Next, we will present the mathematical formulation of the ICLV model39,40.

In our application, we have four distinct adoption groups and the discrete choice component of the model estimates the utility associated with belonging to each of these groups, taking into account the explanatory and latent variables. The utility derived by individual \(n\) when belonging to group \(i\) is described by Eq. (1).

$${U}_{n,i}= {V}_{n,i}+ {\epsilon }_{n,i}$$

(1)

Traditionally, in a discrete choice model, \({U}_{n,i}\) is the stochastic utility that is a function of the systematic utility \(({V}_{n,i})\) and an error term \({(\epsilon }_{n,i})\) following a type I extreme value distribution. However, in an ICLV, \({V}_{n,i}\) is also stochastic because of the random effects incorporated via latent variables, as represented in Eqs. (2) and (3).

$$V_{n,i} = \delta_{n,i} + \beta_{i} x_{n,i} + \lambda_{i} \alpha_{n,i}$$

(2)

where \({\delta }_{n,i}\) is an alternative specific constant, \({x}_{n,i}\) is the vector of observed variables, for example, sociodemographic variables (in this study we only have individual related explanatory variables and no alternative specific variables). \({\alpha }_{n,i}\) refers to latent variables, and \({\beta }_{i}\) and \({\lambda }_{i}\) are vectors of estimated coefficients.

The latent variable component of the model contains structural and measurement models. The structural model part is shown in Eq. (3) and it estimates a vector of parameters (\({\gamma }_{l})\) showing how sociodemographic variables \({(z}_{n})\) influence attitudes and preferences, which are represented by the latent variables (\({\alpha }_{n,l}\)), while \({\eta }_{n,l}\) is the associated random disturbance that follows a standard normal distribution across individuals (\(l\) =1,…, L, where L is the total number of latent variables, in our case 2).

$$\alpha_{n,l} = \gamma_{l} z_{n} + \eta_{n,l}$$

(3)

The measurement model part uses an ordered logit model to construct the latent variables based on the responses to 5-level Likert scale attitudinal indicators (\(s\)). The measurement model is presented in Eq. (4).

$$L_{{I_{n,s} }} = \mathop \sum \limits_{p = 1}^{5} \delta_{{I_{n,s,p} }} \left( { \frac{{e^{{\tau_{{I_{s,p} }} – \zeta_{l,s} \alpha_{n,l} }} }}{{1 + e^{{\tau_{{I_{s,p} }} – \zeta_{l,s} \alpha_{n,l} }} }} – \frac{{e^{{\tau_{{I_{s,p – 1} }} – \zeta_{l,s} \alpha_{n,l} }} }}{{1 + e^{{\tau_{{I_{s,p – 1} }} – \zeta_{l,s} \alpha_{n,l} }} }}} \right)$$

(4)

\({L}_{{I}_{n,s}}\) represents the likelihood of the observed value of an individual’s response \({I}_{n,s}\) to an indicator (\(s\)). \({\delta }_{{I}_{n,s,p}}\) takes the value of 1 if individual n selects response p (p: 1…,5) for indicator s. The parameter \({\tau }_{{I}_{s,p}}\) is estimated as the threshold value, where the normalisation condition for \({\tau }_{{I}_{s,0}}\) is set to -∞ and \({\tau }_{{I}_{s,5}}\) is set to + ∞ so that we estimate the four intermediate values. \({\zeta }_{l,s}\) estimates the impact of \({\alpha }_{l}\) on \({I}_{s}\). A significant estimate for \({\zeta }_{l,s}\) shows that the latent attitude \({\alpha }_{l}\) has a statistically significant impact on the answers provided to the attitudinal question \({I}_{s}\).

Equation 5 represents the equation that calculates the likelihood of individual n belonging to group \(i\), conditional on \({\alpha }_{n}\) and \(\beta\).

$$L_{{C_{n} }} \left( {\beta ,\alpha_{n} } \right) = \frac{{e^{{V_{n,i} }} }}{{\mathop \sum \nolimits_{j = 1}^{4} e^{{V_{n,j} }} }}$$

(5)

The joint log-likelihood of all model components is given in Eq. (6). Both of the model components, the component relating to the choice and the component related to attitudinal questions are a function of latent variable. This is why to jointly estimate these models, the entire likelihood function is integrated over the random component in the latent variable (\({\eta }_{n}\)). The Apollo R-programming package is utilized for estimating the ICLV model41.

$${\text{LL}}=\sum_{n=1}^{N}{\text{log}}\int {L}_{{C}_{n}}(\beta ,{\alpha }_{n})\prod_{s=1}^{8}{L}_{{I}_{n,s}} \Phi ({\eta }_{n})d{\eta }_{n}$$

(6)

To mitigate the potential for reaching local maxima during estimation, we utilised a search strategy recommended in the Apollo package41. This involved employing multiple starting points to systematically eliminate unlikely solutions and enhance the probability of discovering the optimal one.

Data

The data used for this analysis is open source and were collected through a web-based survey conducted by Essential Research for Energy Consumers Australia in 2022. The target group for the survey consisted of individuals who are heads of households, aged 18 and above, and actively involved in decision-making regarding electricity and gas matters within their households across Australia. This survey is conducted annually and explores the attitudes and activity of residential energy consumers by asking questions about how they use power and energy technologies, their attitude to new technology, and their view on the future of energy. For further information, please refer to the details provided by Energy Consumers Australia on their website42. In this section, we highlight the survey aspects pertinent to the present analysis, as listed in section “Method and data”.

The choice variable is not the outcome of a direct question in the survey and is defined based on respondents’ PV and EV adoption status. PV and EV adoption were elicited separately using four possible options: (1) currently own, (2) intending to purchase in the next 12 months, (3) considering, but not intending to purchase in the next 12 months, and (4) not intending to purchase. To define the outcome variable of our model, respondents who owned, intended, and considered buying PV and EV were categorised as adopting both PV and EV, respondents who owned, intended, or considered buying EV but did not intend to purchase PV were categorised as only adopting EV. Analogously, respondents who owned, intended, or considered buying PV but did not intend to purchase an EV were categorised as only adopting PV. Lastly, respondents who did not intend to buy an PV nor an EV were categorised as adopting none. The decision to merge current adopters/owners with those intending and considering adoption was driven by the aim of obtaining a comprehensive understanding of the adoption landscape and the factors influencing adoption decisions across different stages. By solely focusing on current owners, we would miss out on understanding the needs and motivations of individuals who are considering and intending to adopt PV and/or EV technologies (and we would have a myopic perspective of early adopters only). By including them in the same category as current owners, we can develop more effective policies that consider the necessities of prospective adopters, potentially accelerating the overall adoption process. Additionally, merging these categories increases the sample size of the different groups, resulting in improved statistical power and analysis reliability (helping to mitigate biases that may arise from small sample sizes). This is particularly crucial in Australia, where the current number of EV owners is relatively low.

Sample description

Table 1 presents the descriptive statistics of dependent and explanatory variables for the final clean sample, consisting of 2219 respondents. Most respondents are still not willing to adopt PVs or EVs, with the smallest group being those who are willing to adopt only EVs and not PVs. This pattern is expected in Australia, where EV adoption is lagging but PV adoption rates are relatively high (33%, according to the Australian PV Institute7).

Table 1 Descriptive statistics of dependent and explanatory variables.

According to the data providers, the sample is considered representative of the population of household heads in terms of age and gender. However, it is worth noting that we do not have access to the specific distribution of this population from Census data. Therefore, we compare the distribution of selected sociodemographic characteristics in the sample with that of the Australian driving age population43,44. The sample shows a slightly higher share of women and a lower share of young adults (between 18 and 34 years old) compared to the driving age population. This is an expected difference considering that the survey targeted individuals who make electricity-related decisions in the household. While the share of home owners and renters is equivalent to the population, we observe an underrepresentation of multi-unit building dwellers45. This limitation is particularly relevant to the problem investigated in this study because both PV installation and EV charging face more significant barriers in apartment complexes. An aggregate analysis based on this sample would thus be biased, but by using a disaggregate model that can capture both observed and unobserved individual heterogeneity we expect to extract important insights even for this underrepresented group.

Regarding household energy and appliances related variables, smart meter ownership rates are much higher than home energy management system ownership rates, with 40.7% of respondents having a smart meter and only 9.6% having an energy management system. The discrepancy in ownership rates between smart meters and home energy management systems in Australia can be largely attributed to the extensive rollout of smart meters facilitated by government initiatives and utility companies, especially in Victoria46. Additionally, the perceived benefits of smart meters, such as accurate and real-time energy usage information, maybe more widely recognized and valued by consumers compared to the advanced features offered by home energy management systems. A relatively low percentage of households own electricity-intensive appliances, with 11.8% owning swimming pools or spa pools and 22.5% having three or more refrigerators.

Table 2 displays the attitudinal indicators and their response distributions, which were measured on a five-point Likert scale. Respondents were asked to choose from five levels of agreement, from strongly disagree to strongly agree. For analysis purposes, we assigned a value of 5 to the highest level of favourable attitudes toward the environment and technology, and a value of 1 to the least favourable level. The last four indicators of environmental attitudes had their scale inverted to ensure that they were all capturing the underlying construct monotonically. Respondents generally express a desire for action on climate change issues. Regarding technology attitudes, only 6% consider themselves early adopters, while around 20% are strongly inclined to use technology to manage bills and learn about new ways of generating, storing, and distributing electricity.

Table 2 Frequency of response to ten attitudinal indicators.

Factor analysis

We conducted an exploratory factor analysis using Principal Axis Factoring to examine the relationships among the indicators and identify latent variables. Two factors emerged, both demonstrating strong consistency, as all loadings surpassed the threshold of 0.4. We labelled the first factor ‘Pro-Environmental Attitude,’ as it comprised all five indicators related to environmental attitudes. The second factor, which included all three indicators associated with technology attitudes, was named ‘Technology Interest.’ The outcomes of the factor analysis, including the two resulting factors and the rotated factor loadings after Varimax rotation, are presented in Table 3. It is important to note that the factor analysis coefficients were not utilized in the model; the factor analysis was solely conducted to identify the optimal indicators for the latent constructs.

Table 3 Factor analysis on two latent variables.

Nextracker Launches Industry’s First Low Carbon Solar Tracking Solution

Third-party Life Cycle Assessment (LCA) methodology confirms a reduced carbon footprint of up to 35% over the entire product life cycle for the Nextracker NX Horizon™ low carbon solution.

The opening orders demonstrate the solar industry’s demand for decarbonized solutions

Nextracker (Nasdaq: NXT), a global provider of intelligent solar tracking and software solutions, today announced the availability of its flagship NX Horizon™ solar tracking system with up to 35% less carbon footprint. Marking a major milestone for solar energy, the NX Horizon low-carbon tracker solution rounds out Nextracker’s technology leadership and robust supply chain solutions, including the use of electric arc furnace (EAF) manufacturing, recycled steel and logistics strategically located near project sites.

This press release has multimedia features. See the full release here: https://www.businesswire.com/news/home/20240423430902/en/

Nextracker introduces the industry’s first solar tracker with a carbon footprint reduced by up to 35 percent (Photo: Nextracker)

The low-carbon tracker solution initially proposed in the United States includes Life Cycle Assessment (LCA) documentation using a third-party validated analysis of environmental benefits, including reductions in carbon footprint, land use, water consumption and other source-related metrics. manufacturing, delivery and operation of solar trackers. Nextracker has also achieved Carbon Trust1 Product Carbon Footprint label certification for the NX Horizon low-carbon solar tracker system, demonstrating that it meets the global standard for collecting, evaluating and reporting carbon emissions data throughout the lifecycle of solar trackers.

“Our low carbon tracker delivers measurable results in solar decarbonisation through circular and increasingly renewable steelmaking, optimized logistics and careful selection of raw material suppliers,” he said. Dan Shugar, Founder and CEO of Nextracker. “We appreciate our customers helping us move toward a cleaner, more resilient supply chain to ensure solar providers and consumers have third-party verification to meet Scope 3 and other decarbonization goals and requirements.”

Nextracker has secured pre-orders for its newly launched NX Horizon low-carbon tracker from several solar customers – both of which have committed to low-carbon solar tracker systems for US-based solar projects.

“Nextracker’s commitment to developing low carbon solutions exemplifies the innovation and leadership needed to move the energy sector forward. At Sol Systems, we are proud to collaborate with partners like Nextracker who share our vision for a cleaner, more sustainable energy landscape. Together, we not only promote low carbon technologies; we set the standards for tomorrow’s energy infrastructure” Yuri Horwitz, co-founder and CEO of Sol Systems.

“Upstream control for the supply of sustainably produced steel and components is highly complex and requires expertise and deep partnerships to navigate,” he said. Kevin Smith, CEO of Arevon. “Nextracker continues to be the industry leader in operational excellence combined with solar tracker engineering design that significantly reduces the carbon footprint for our utility-scale solar projects.”

Nextracker is at the forefront of implementing sustainable supply chain solutions for the energy sector through managed upstream of raw materials and local manufacturing for solar tracking systems. Electric Arc Furnace (EAF) processing uses recycled steel and electricity to create new steel and significantly reduces greenhouse gas emissions compared to traditional Basic Oxygen Furnace (BOF) operations based on iron ore and coal. Not only does this align with Nextracker’s commitment to minimizing environmental impact, it also leverages American rebar steel supplies, enabling resource efficiency and growth for the US steel industry. Nextracker has also reduced carbon intensive2 materials such as aluminum from its NX Horizon low carbon offering to below 1% aluminum by weight in the product.

Jon Moore, CEO of research firm BloombergNEF: “Industry accounts for more than 20% of global greenhouse gas emissions; steel alone accounts for 7%, making it an important industry for decarbonisation. According to our analysis, the steel sector invested $35 billion in clean capacity globally last year, but more is needed to achieve net zero. “Green steel demand is an important signal to steelmakers that they need to bring new, low-carbon capacity online and accelerate the transition to net-zero.”

The new NX Horizon low carbon tracker offering is available with first deliveries scheduled for later this year. Visit our website for more information.

In Nextracker CLEANPOWER 2024

Learn more at this year’s CLEANPOWER (ACP) conference, May 6-9, 2024 at the Minneapolis Convention Center from Nextracker panel speakers for discussion on the following topics:

“Local Clean Energy Production” Dan Shugar, Founder and CEO of Nextracker (May 7, 2:15-3:15 p.m. CT, Charge Up Theater). “Solar Supply Chain” Alejandro Riofrio, Vice President, Supply Chain North America, Nextracker (May 8, 2:15-3:15 p.m. CT, Charge Up Theater).

About Nextracker

Nextracker is a leading provider of intelligent, integrated solar tracker and software solutions used in utility-scale and distributed generation solar projects worldwide. Its products allow solar panels to track the movement of the sun across the sky and optimize plant performance. With plants operating in over thirty countries around the world, Nextracker offers solar tracker technologies that increase energy production while reducing costs for significant plant ROI. For more information, visit Nextracker.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, including statements regarding future orders, demand for Nextracker’s offerings and planned shipments of Nextracker products. These forward-looking statements are based on various assumptions and the current expectations of Nextracker management. These statements involve risks and uncertainties that could cause actual results to differ materially from those anticipated in these forward-looking statements, including the risks and uncertainties described in “Risk Factors” and “Management’s Discussion and Analysis of Financial Condition and Results of Operations” . In Nextracker’s most recent Quarterly Report on Form 10-Q, Annual Report on Form 10-K and other documents that Nextracker has filed or will file with the Securities and Exchange Commission. There may be additional risks of which Nextracker is not aware or which Nextracker currently believes to be immaterial, which could cause actual results to differ from forward-looking statements. Readers are cautioned not to place undue reliance on these forward-looking statements. Nextracker undertakes no obligation to update these forward-looking statements.

1 The Carbon Trust is an independent organization that provides verification of sustainable development achievements. The Carbon Trust’s Product Carbon Footprint label certifies that a brand has worked to measure and reduce a product’s carbon emissions. LCA is a method that assesses the environmental impact of a product’s life cycle through data collection and evaluation. It considers all stages of a product’s life cycle, including raw material extraction, production, use and disposal. LCA also records and calculates the greenhouse gases produced during the product’s life cycle.

2 International Energy Agency, 2023, https://www.iea.org/energy-system/industry/aluminum

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