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By comparing regression models, this study aims to analyze the added home value of green sustainability features and green efficiency characteristics, rather than green certifications, in the city of Austin.

The adoption of home green energy efficiency upgrades has emerged as a new trend in the real estate industry, offering several benefits to builders and home buyers. These include tax reductions, health improvements and energy savings. Previous studies have shown that energy-certified single-family homes command a premium in the marketplace. However, the literature is limited in its analysis of the effects of green upgrades and certification on different types of single-family homes. To address this gap, this research collected data from 21,292 multiple listing services (MLS) closed home-selling listings in Austin, Texas, over a period of 35 months.

The analysis results showed that green efficiency features could generally increase single-family housing prices by 11.9%, whereas green sustainability upgrades can potentially bring a 11.7% higher selling price. Although green housing certification did not have significant effects on most housing groups, it did increase closing prices by 13.2% for single-family residences sold at the medium price range, which is higher than the impacts from simply listing the green features on MLS.

The study contributes to the body of knowledge by examining the market value of broadly defined energy efficiency and sustainability features in the residential housing market. The findings can help policymakers, brokerage firms, home builders and owners adjust their policies and strategies related to single-family home sales and mortgage approvals. The research also highlights the potential benefits of capitalizing on green housing features other than certifications.

Given the importance of green finance in a discussion of energy efficiency and clean energy, it is critical to evaluate its implications for the growth of renewable energy. This study examines the impact of green finance on renewable energy development in Singapore.

The dynamic ordinary least squares (DOLS) regression was used in this work to test such a connection.

Using the DOLS for the period 2000–2020, it was discovered that green finance aids renewable energy development in Singapore. Additionally, the findings revealed that economic growth, oil prices, energy consumption, carbon dioxide emissions and institutional factors are all positively associated with renewable energy growth, resulting in a boost in renewable energy development.

Hence, as a result, the monetary authorities of Singapore, such as financial institutions, non-governmental organisations and corporations, should prioritise renewable energy projects under green finance initiatives to boost renewable energy growth. This may assist in raising investment flows to green projects; hence, accelerating the adoption of renewable energy.

Increased Singapore's initiatives to accelerate green finance have prompted this study to examine the research question of whether green finance has a significant impact on renewable energy growth. Thus, to the best of the authors’ knowledge, this will be the first empirical study to explore the impact of green finance on renewable energy growth in the case of Singapore.

Using building information modelling (BIM) technology, a conventional structure in this study was converted into a green building to measure its energy usage and CO2 emissions.

Digital images of the existing building conditions were captured using unmanned aerial vehicle (UAV), and were fed into Meshroom to generate the building’s geometry for 3D parametric model development. The model for the existing conventional building was created and converted to an energy model and exported to gbXML in Autodesk Revit for a whole building analysis which was carried out in the Green Building Studio (GBS). In the GBS, the conventional building was retrofitted into a green building to explore their energy consumption and CO2 emission.

By comparing the green building model to the conventional building model, the research found that the green building model saved 25% more energy while emitting 46.8% less CO2.

The study concluded that green building reduces energy consumption, thereby reducing the emission of CO2 into the environment. It is recommended that buildings should be simulated at the design stage to know their energy consumption and carbon emission performance before construction.

Occupant satisfaction, operation cost and environmental safety are essential for sustainable or green buildings. Green buildings increase the standard of living and enhance indoor air quality.

This investigation aided in a pool of information on how to use BIM methodology to retrofit existing conventional buildings into green buildings, showing how green buildings save the environment as compared to conventional buildings.

Switching to green energy is a crucial step in achieving carbon neutrality. This study aims to explore what motivates people to use green energy and how much more people are willing to pay for green energy.

Grounded on the value–attitude–intention hierarchy, this study proposes that environmental consciousness as a human value influences attitudes including attitude toward environmental issues and attitude toward ecosocial benefits while attitudes, information and knowledge about green energy and quality and price of green energy influence people’s intention to use green energy. Data were collected from 342 Chinese adults.

Results showed that environmental consciousness significantly and positively influenced attitudes while attitude toward environmental issues had the greatest effect on people’s intention to use green energy, followed by quality and price of green energy. About 44% respondents were willing to pay 2.5% to <5% more money for green energy.

This study extended the use of value–attitude–intention hierarchy to investigate what motivates people to use green energy. Specifically, this study demonstrated that quality and price, and knowledge and information also significantly shaped people’s intention to use green energy significantly.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

The COVID-19 pandemic has generated a crisis that has hit the European economy and the currently existing systems. To cope with the crisis, Europe has started an investment aiming the energy transition and crisis. Portugal, Spain and Greece have received the approval of their National Recovery Plans from the European Commission, with a definition of spending up to 2026 through the European Union (EU) Next Generation Found. The study investigates whether the Green Deal policies are relaunched by the plans financed and whether the pandemic has changed and conditioned the priorities of the energy transaction. The study uses the lens of corporate social responsability (CSR) and relapse measurable across the Sustainable Development Goals (SDGs).

The Green Deal policies supported by new European investments in the three countries were analyzed through a content analysis (CA) technique to investigate the associated practical and theoretical elements.

The energy theme has a relevance compared to other issues in the investment plans envisaged in Greece, Portugal and Spain. The analysis highlights energy efficiency, sustainable energy and reduction of consumption among the main themes. Energetically, sustainable building plays a central role. The study highlights the relationship between Green Deal policies, CSR, SDGs and management tools adopted.

The study strengthens the relationship between the Green Deal, CSR and SDGs by identifying policies that have already been implemented and theoretical and practical gaps on which politicians and scholars will have to investigate and support in the process of development and continuity of the identified pillars.

This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.

The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.

From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.

The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.

The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.

This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

Accelerating the green economy’s transition is a practical means of lowering emissions and conserving energy, and its effects on the greenhouse effect merit careful consideration. Growing environmental deterioration has compelled decision-makers to prioritize sustainability alongside economic growth. Policymakers and the business community are interested in green investment (GRE), but its effects on social and environmental sustainability are still unknown. Based on this, this study aims at looking into the time-frequency interplay between GRE and carbon dioxide emissions and assessing the impacts of economic growth, financial globalization and fossil fuel energy (FUE) usage on this nexus in Vietnam across different time and frequency domains.

The authors employ continuous wavelets, cross wavelet transforms, wavelet coherence, Rua’s wavelet correlation and wavelet-based Granger causality tests to capture how the domestic variance and covariance of two-time series co-vary as well as the co-movement interdependence between two variables in the time-frequency domain.

The results shed new light on the fact that GRE will increase the levels of environmental quality in Vietnam in the short and medium run and there is a bidirectional causality between the two indicators across different time and frequencies. In addition, when the authors observe the effect of economic growth, financial globalization and fossil fuel energy consumption on this interplay, the findings suggest that, in different time and frequencies, any joined positive change in these indicators will move the CO₂ emissions-GRE nexus.

Policymakers and governments can greatly benefit from this topic by utilizing the function of economic institutions in capital control of GRE and CO₂ emissions and modifying the impact of GRE on the greenhouse effect by accelerating the green growth of economic industries.

The current work contributes to the current literature on GRE and CO₂ emissions in several dimensions: (1) considering the sustainable development in Vietnam, by employing a new single-country dataset of GRE index, this paper aims to contribute to the growing body of research on the factors that influence CO₂ emissions, as well as to provide a detailed explanation for the relationship between GRE and CO₂ emissions; (2) localized oscillatory components in the time-domain region have been used to evaluate the interplay between GRE and CO₂ emission in the frequency domain, overcoming the limitations of the fundamental time-series analysis; (3) the mediation role of economic growth, financial globalization and FUE in affecting the GRE-CO₂ relationship is empirically explored in the study.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.