Prospective Research Trend Analysis on Zero-Energy Building (ZEB): An Artificial Intelligence Approach
Round 1
Reviewer 1 Report
The authors have conducted an objective review of the literature on Zero-Energy Building (ZEB) using an Artificial Intelligence Approach, specifically Natural Language Processing (NLP). Findings of the study have implications for future studies, and they could be relevant to practitioners. That said, I would like to make some comments to improve the manuscript:
1. The abstract should be revised by highlighting the practical and theoretical implications of the study. Since this is a review study, the theoretical implications should state the direction for future empirical studies. Besides, the practical implications should emphasise the relevance of the research findings to practitioners and policymakers.
2. Comments on introduction: On page 2, lines 94-97: "Specifically, this study adopted a methodology of analysing R&D grant documents..." The authors are encouraged to provide adequate justifications for choosing natural language processing (NLP) over traditional methodology of bibliometrics or scientometrics.
3. On page 3, line 116, is there any justification for limiting the literature search from 2000 to 2022 ? Is it that most publications on ZEB started in 2000?
4. I have noticed that the authors have briefly mentioned the relevance of the findings under this section. However, since this section also indicates "Discussion", the authors are entreated to discuss the findings.
5. Could the clusters be named objectively using the NLP or any other algorithm? That could add valuable information to the manuscript.
Author Response
The authors have conducted an objective review of the literature on Zero-Energy Building (ZEB) using an Artificial Intelligence Approach, specifically Natural Language Processing (NLP). Findings of the study have implications for future studies, and they could be relevant to practitioners. That said, I would like to make some comments to improve the manuscript:
1. The abstract should be revised by highlighting the practical and theoretical implications of the study. Since this is a review study, the theoretical implications should state the direction for future empirical studies. Besides, the practical implications should emphasise the relevance of the research findings to practitioners and policymakers.
>> The abstract has been revised as follows.
Abstract: While global attention to zero-energy building (ZEB) has surged as a sustainable countermeasure to high-energy consumption, a congruent expansion in research remains conspicuously absent. Addressing this lacuna, our study harnesses public research and development grant data to decipher evolving trajectories within ZEB research. Distinctively departing from conventional methodologies, we employ state-of-the-art natural language processing (NLP) artificial intelligence models to meticulously analyze grant textual content pertinent to ZEB. Our findings illuminate an expansive spectrum of ZEB-related research, with a pronounced focus on the holistic continuum of energy supply, demand, distribution, and actualization within archi-tectural confines. Theoretically, this work delineates key avenues ripe for future empirical ex-ploration, fostering a robust academic foundation for subsequent ZEB inquiries. Practically, the insights derived bear significant implications for practitioners, informing optimal implementa-tion strategies, and offering policymakers coherent roadmaps for sustainable urban develop-ment. Collectively, this study affords a panoramic perspective on contemporary ZEB research contours, enhancing both scholarly comprehension and practical enactment in this pivotal do-main.
2. Comments on introduction: On page 2, lines 94-97: "Specifically, this study adopted a methodology of analysing R&D grant documents..." The authors are encouraged to provide adequate justifications for choosing natural language processing (NLP) over traditional methodology of bibliometrics or scientometrics.
>> The following has been added on page 3, lines 101 to 109:
The decision to employ AI-driven NLP techniques in lieu of conventional scientometric analysis stems from several considerations. Firstly, a plethora of studies have already employed scientometric or bibliometric methodologies to probe into ZEB-related research and development trends [29-31]. Adopting a similar approach would, thus, dilute the novelty of this investigation. Secondly, recent advancements in NLP, particularly methodologies harnessing architectures like BERT and Transformer, have demonstrated remarkable efficiency. Consequently, this study posits that leveraging these cutting-edge AI methodologies for analyzing ZEB-related R&D trends can yield more granular and empirically robust insights.
3. On page 3, line 116, is there any justification for limiting the literature search from 2000 to 2022 ? Is it that most publications on ZEB started in 2000?
>> The following has been added on page 3, lines 143-147:
Data collection post-2000 was strategically chosen to discern shifts in R&D trends encompassing early ZEB-related research up to contemporary advancements. This decision was made in light of the fact that the Kyoto Protocol, an important international treaty aimed at mitigating global warming, was adopted in 1997.
4. I have noticed that the authors have briefly mentioned the relevance of the findings under this section. However, since this section also indicates "Discussion", the authors are entreated to discuss the findings.
>> The conclusions and discussion have been separated and rewritten.
4. Discussion
The clustering results highlight the diverse range of ZEB research, covering a spectrum from advanced nuclear technology to fluid dynamics in complex systems. Document categorization into 25 distinct groups signifies the breadth of subjects under the purview of ZEB research. Cluster 0 points to a nascent inclination towards harnessing advanced nuclear technology for sustainable ZEB outcomes. The focus on innovative reactor designs and advanced diagnostic tools alludes to a potential pivot towards nuclear energy as a sustainable solution for zero-energy buildings. This warrants an in-depth exploration of its feasibility, associated ramifications, and public perception. Cluster 1 accentuates the relevance of innovations in material science and the imperative of seismic safety in ZEB. Given the mounting concerns over environmental calamities, there's an increased emphasis on seismic design techniques and ensuring the resilience of towering structures. It's essential to evaluate how such advancements might redefine the established architectural and engineering paradigms in ZEB. The attention on fluid dynamics, as highlighted in Cluster 2, is of particular interest. When explored in the context of aero-dynamics and atmospheric physics, fluid dynamics can profoundly impact building design, ventilation strategies, and energy conservation approaches. Delving into the interplay between these elements and ZEB design promises fresh perspectives.
The employment of AI tools, like BERT and UMAP, facilitates a nuanced exploration of ZEB-focused R&D trends. Yet, it remains critical to reflect on potential biases, ascertain the results' reliability, and acknowledge the limitations of this AI-driven approach. The ungrouped outliers could represent untapped knowledge, potentially pointing to emerging or niche research areas on the verge of broader recognition. An in-depth analysis of these outliers could delineate emerging directions in ZEB research. Using AI to investigate prospective research trends in ZEB has demarcated clear clusters echoing the field's multifaceted nature. Each cluster epitomizes a distinct aspect of ZEB, shedding light on the intricate avenues of sustainable building design research. Nevertheless, while these clusters serve as a valuable guide to current ZEB research, inherent limitations and potential areas of deeper inquiry emerge.
Despite BERT's proven effectiveness in text embedding, biases innate to its pre-trained model can creep in. Moreover, while UMAP is proficient at dimensionality reduction, its sensitivity to hyperparameters might skew the clustering outcome. Furthermore, HDBSCAN's capability in capturing diverse density clusters might occasionally miss more diffuse ones, categorizing certain research areas as outliers.
The study's focus on only the titles and abstracts of R&D grants may inadvertently neglect subtle nuances or emergent themes present in the full text. Additionally, as this study provides a snapshot of ZEB trends, it may not trace the entire thematic evolution, especially emerging or waning research facets. Outliers, which don't neatly fit into the predefined 25 clusters, could be hinting at avant-garde, cross-disciplinary, or specialized research trajectories. An exhaustive qualitative probe into these outliers might unearth pioneering ZEB research directions.
Undertaking a time-based examination of the R&D grants might illuminate the temporal evolution of ZEB research. Such an inquiry can chronicle the birth, growth, and possible waning of distinct research themes, furnishing a fluid overview. Subsequent studies stand to gain from extending their scope to the full text of R&D grants, thereby ensuring a more holistic grasp of the research nuances. Complementing this with external datasets, like citation networks or patent databases, would bestow a comprehensive perspective on the ZEB research's impact and innovative pathways.
5. Conclusions
Energy consumption in the building and construction sectors remains a salient challenge across many nations. Amid the global agreement to transition from fossil fuels to renewable energies, Zero-Energy Building (ZEB) stands out as a viable alternative, garnering extensive research attention [1-2]. This research introduced an AI-driven methodology to examine ZEB-centric R&D grants, aiming to decipher future trajectories and enrich our understanding of the discipline.
Diverging from conventional scientific analyses that primarily focus on academic articles, our study prioritized R&D projects to illuminate the prospective avenues of ZEB research. Despite inherent challenges in analyzing all ZEB-related R&D undertakings due to data accessibility constraints, such a methodological choice is pivotal. These projects often epitomize national R&D agendas, overseen by governmental or public entities. Our analysis emphasized concerted efforts to amplify ZEB efficiency, elevate photovoltaic performance, and blueprint smart cities integrating ZEB with transportation and avant-garde technologies, such as ICT and sensors. These insights resonate with Rotolo et al. [36], underscoring the relevance of R&D funding data in spotlighting imminent research directions. The value proposition of our study lies in its novel methodology to envisage the ZEB research horizon. By leveraging R&D project data, we offer a holistic vantage point and insights distinct from those gleaned through traditional article analyses. Moreover, this endeavor affirms the potency of AI techniques as instrumental scientific apparatuses, thereby extending the frontiers of such inquiries.
Our findings can serve both theoretical and applied facets, assisting entities in strategizing R&D initiatives, budget allocations, and outcome evaluations. Furthermore, this work provides a scaffold for assessing the contemporary status and prognosticating ZEB research's forthcoming trends. Nonetheless, certain limitations persist, such as potential miscategorization of R&D grants or human biases affecting thematic assessments. Pioneering language models, exemplified by OpenAI’s ChatGPT or Google’s Bard, could proffer resolutions in future research endeavors.
There exists an imperative for subsequent studies to explore the intrinsic attributes and the juxtaposition of R&D grant data with scientific publications. Such endeavors would accentuate the significance of R&D grant data, augmenting its analytical utility, and paving the way for diverse AI-driven scientometric evaluations. Our AI-enhanced assessment underscored the multifaceted nature of ZEB research, spanning domains from nuclear technology to material science. The emphasis on fields such as advanced nuclear technology heralds potential paradigm shifts in ZEB's energy and sustainability blueprints, signaling a renaissance in sustainable architectural design.
Based on the derived clusters, it is prudent for stakeholders to channel investments into emergent domains, such as cutting-edge materials, seismic safety paradigms, and novel nuclear innovations, as these could dictate ZEB's future trajectory. This work epitomizes AI's prowess in sifting through and categorizing intricate research vectors, suggesting that embracing such tools can refine the granularity and scope of scientific evaluations, thereby equipping stakeholders with actionable insights. Given the fluidity of ZEB research, a periodic reassessment of these trends becomes indispensable. A steadfast monitoring regimen, buttressed by state-of-the-art methodologies, is quintessential to ensure alignment with evolving technological advances and societal imperatives. A meticulous exploration of anomalies or outliers could also proffer a visionary perspective on the research frontier of ZEB.
By comprehensively mapping the ZEB terrain through AI, this study proffers indispensable insights for a broad audience, ranging from researchers and policymakers to industry frontrunners. The ever-evolving tapestry of ZEB research necessitates sustained scrutiny and recalibration to propel sustainable and trailblazing architectural innovations.
5. Could the clusters be named objectively using the NLP or any other algorithm? That could add valuable information to the manuscript.
>> On page 14, lines 344-347, the following has been added:
Utilizing the BERTopic model, a state-of-the-art topic modeling technique, we extracted key thematic elements to inform the titles of each cluster. These titles were formulated based on keyword prominence and their relevance to the overarching themes of the respective R&D grants.
Additionally, the name of each cluster is additionally specified in Table 6.
Cluster 0 Advanced nuclear technologies & applications for ZEB
Cluster 1 Seismic safety, energy efficiency, and sustainable building design
Cluster 2 Fluid dynamics and turbulence modeling for ZEB
Cluster 3 Advanced photovoltaic technologies and integration for ZEB
Cluster 4 Low-carbon cementitious products and innovations
Cluster 5 Sustainable production and advanced tech integration
Cluster 6 Geothermal energy and thermal storage systems
Cluster 7 Power electronics in ZEB
Cluster 8 Energy generation, storage, and conversion technologies
Cluster 9 Ultralow power electronic circuits and hardware
Cluster 10 Energy-efficient devices for smart systems
Cluster 11 Nanotechnology and advanced material systems for ZEB
Cluster 12 Advanced materials and technologies for ZEB and transportation
Cluster 13 AI-driven marine ecosystem interactions and sustainable energy monitoring
Cluster 14 Zero-emission solutions for sustainable transportation
Cluster 15 Hydrogen solutions and carbon management for ZEB
Cluster 16 Combustion systems and alternative fuels for ZEB
Cluster 17 Water-based renewable energy and storage technologies
Cluster 18 Sustainable ZEB solutions in water treatment and industrial manufacturing
Cluster 19 Advanced window systems for energy-efficient buildings
Cluster 20 Retrofitting and energy optimization in existing buildings
Cluster 21 Wood-centered approaches for ZEB and sustainable constructions
Cluster 22 Holistic ZEB development and urban-scale energy optimization
Cluster 23 ZEB technologies and building-integrated renewable systems
Cluster 24 Advanced energy systems and sustainable building innovations
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The novelty of this paper can be published in the Sustainability. The topic is interesting; therefore, my decision is an acceptance with minor revision.
Here are my comments on improving the manuscript:
1. Abstract: This section lacks research findings and contributions. Please kindly update.
2. Introduction:
- To clarify the introduction, please consider stating the research questions and objectives.
3. Discussion and Conclusions:
- The discussion is still poor due to a lack of thorough analysis on limitations and solid recommendations for practical future research. Please kindly update.
- Moreover, please separate the discussion and conclusion sections to provide a thorough discussion and clarify them.
4. Conclusion:
- Please rewrite this section separately with a discussion section, focusing on research contributions and limitations as well as emphasizing future work. Please update.
Author Response
The novelty of this paper can be published in the Sustainability. The topic is interesting; therefore, my decision is an acceptance with minor revision.
Here are my comments on improving the manuscript:
1. Abstract: This section lacks research findings and contributions. Please kindly update.
>> The abstract has been revised as follows.
Abstract: While global attention to zero-energy building (ZEB) has surged as a sustainable countermeasure to high-energy consumption, a congruent expansion in research remains conspicuously absent. Addressing this lacuna, our study harnesses public research and development grant data to decipher evolving trajectories within ZEB research. Distinctively departing from conventional methodologies, we employ state-of-the-art natural language processing (NLP) artificial intelligence models to meticulously analyse grant textual content pertinent to ZEB. Our findings illuminate an expansive spectrum of ZEB-related research, with a pronounced focus on the holistic continuum of energy supply, demand, distribution, and actualization within architectural confines. Theoretically, this work delineates key avenues ripe for future empirical exploration, fostering a robust academic foundation for subsequent ZEB inquiries. Practically, the insights derived bear significant implications for practitioners, informing optimal implementation strategies, and offering policymakers coherent roadmaps for sustainable urban development. Collectively, this study affords a panoramic perspective on contemporary ZEB research contours, enhancing both scholarly comprehension and practical enactment in this pivotal domain.
2. Introduction:
- To clarify the introduction, please consider stating the research questions and objectives.
>> On page 3, lines 111-137, the following has been added:
The research questions accordingly are as follows.
(1) How does the trend analysis of ZEB research using R&D grant data differ from those derived from scientific publication data?
(2) What technological opportunities can be observed for the future-oriented directions of ZEB-related R&D using grant data from major countries?
(3) What is the current status of global ZEB R&D based on the analysis of R&D grant data?
(4) How does the knowledge structure of ZEB research manifest when evaluated through R&D grant data?
(5) Which future research directions emerge when R&D grant data for ZEB is analysed through NLP based on AI models?
From these research questions, the research objectives presented in this paper are as follows.
(1) To conduct a comprehensive trend analysis of ZEB research using R&D grant data from major countries.
(2) To contrast the insights derived from R&D grant data with those typically obtained from scientific publication data.
(3) To pinpoint technological opportunities that elucidate future-oriented directions in ZEB-related R&D.
(4) To encapsulate the present status of global ZEB R&D by examining invested grant data.
(5) To identify and map out the knowledge structure within the ZEB research domain.
(6) To leverage an innovative methodology employing NLP based on AI models for analyzing R&D grant document data, moving away from conventional biblio-metric or scientometric methods.
3. Discussion and Conclusions:
- The discussion is still poor due to a lack of thorough analysis on limitations and solid recommendations for practical future research. Please kindly update.
- Moreover, please separate the discussion and conclusion sections to provide a thorough discussion and clarify them.
>> The conclusions and discussion have been separated and rewritten.
4. Discussion
The clustering results highlight the diverse range of ZEB research, covering a spectrum from advanced nuclear technology to fluid dynamics in complex systems. Document categorization into 25 distinct groups signifies the breadth of subjects under the purview of ZEB research. Cluster 0 points to a nascent inclination towards harnessing advanced nuclear technology for sustainable ZEB outcomes. The focus on innovative reactor designs and advanced diagnostic tools alludes to a potential pivot towards nuclear energy as a sustainable solution for zero-energy buildings. This warrants an in-depth exploration of its feasibility, associated ramifications, and public perception. Cluster 1 accentuates the relevance of innovations in material science and the imperative of seismic safety in ZEB. Given the mounting concerns over environmental calamities, there's an increased emphasis on seismic design techniques and ensuring the resilience of towering structures. It's essential to evaluate how such advancements might redefine the established architectural and engineering paradigms in ZEB. The attention on fluid dynamics, as highlighted in Cluster 2, is of particular interest. When explored in the context of aero-dynamics and atmospheric physics, fluid dynamics can profoundly impact building design, ventilation strategies, and energy conservation approaches. Delving into the interplay between these elements and ZEB design promises fresh perspectives.
The employment of AI tools, like BERT and UMAP, facilitates a nuanced exploration of ZEB-focused R&D trends. Yet, it remains critical to reflect on potential biases, ascertain the results' reliability, and acknowledge the limitations of this AI-driven approach. The ungrouped outliers could represent untapped knowledge, potentially pointing to emerging or niche research areas on the verge of broader recognition. An in-depth analysis of these outliers could delineate emerging directions in ZEB research. Using AI to investigate prospective research trends in ZEB has demarcated clear clusters echoing the field's multifaceted nature. Each cluster epitomizes a distinct aspect of ZEB, shedding light on the intricate avenues of sustainable building design research. Nevertheless, while these clusters serve as a valuable guide to current ZEB research, inherent limitations and potential areas of deeper inquiry emerge.
Despite BERT's proven effectiveness in text embedding, biases innate to its pre-trained model can creep in. Moreover, while UMAP is proficient at dimensionality reduction, its sensitivity to hyperparameters might skew the clustering outcome. Furthermore, HDBSCAN's capability in capturing diverse density clusters might occasionally miss more diffuse ones, categorizing certain research areas as outliers.
The study's focus on only the titles and abstracts of R&D grants may inadvertently neglect subtle nuances or emergent themes present in the full text. Additionally, as this study provides a snapshot of ZEB trends, it may not trace the entire thematic evolution, especially emerging or waning research facets. Outliers, which don't neatly fit into the predefined 25 clusters, could be hinting at avant-garde, cross-disciplinary, or specialized research trajectories. An exhaustive qualitative probe into these outliers might unearth pioneering ZEB research directions.
Undertaking a time-based examination of the R&D grants might illuminate the temporal evolution of ZEB research. Such an inquiry can chronicle the birth, growth, and possible waning of distinct research themes, furnishing a fluid overview. Subsequent studies stand to gain from extending their scope to the full text of R&D grants, thereby ensuring a more holistic grasp of the research nuances. Complementing this with external datasets, like citation networks or patent databases, would bestow a comprehensive perspective on the ZEB research's impact and innovative pathways.
4. Conclusion:
Please rewrite this section separately with a discussion section, focusing on research contributions and limitations as well as emphasizing future work. Please update.
>> The conclusions and discussion have been separated and rewritten.
4. Discussion
The clustering results highlight the diverse range of ZEB research, covering a spectrum from advanced nuclear technology to fluid dynamics in complex systems. Document categorization into 25 distinct groups signifies the breadth of subjects under the purview of ZEB research. Cluster 0 points to a nascent inclination towards harnessing advanced nuclear technology for sustainable ZEB outcomes. The focus on innovative reactor designs and advanced diagnostic tools alludes to a potential pivot towards nuclear energy as a sustainable solution for zero-energy buildings. This warrants an in-depth exploration of its feasibility, associated ramifications, and public perception. Cluster 1 accentuates the relevance of innovations in material science and the imperative of seismic safety in ZEB. Given the mounting concerns over environmental calamities, there's an increased emphasis on seismic design techniques and ensuring the resilience of towering structures. It's essential to evaluate how such advancements might redefine the established architectural and engineering paradigms in ZEB. The attention on fluid dynamics, as highlighted in Cluster 2, is of particular interest. When explored in the context of aero-dynamics and atmospheric physics, fluid dynamics can profoundly impact building design, ventilation strategies, and energy conservation approaches. Delving into the interplay between these elements and ZEB design promises fresh perspectives.
The employment of AI tools, like BERT and UMAP, facilitates a nuanced exploration of ZEB-focused R&D trends. Yet, it remains critical to reflect on potential biases, ascertain the results' reliability, and acknowledge the limitations of this AI-driven approach. The ungrouped outliers could represent untapped knowledge, potentially pointing to emerging or niche research areas on the verge of broader recognition. An in-depth analysis of these outliers could delineate emerging directions in ZEB research. Using AI to investigate prospective research trends in ZEB has demarcated clear clusters echoing the field's multifaceted nature. Each cluster epitomizes a distinct aspect of ZEB, shedding light on the intricate avenues of sustainable building design research. Nevertheless, while these clusters serve as a valuable guide to current ZEB research, inherent limitations and potential areas of deeper inquiry emerge.
Despite BERT's proven effectiveness in text embedding, biases innate to its pre-trained model can creep in. Moreover, while UMAP is proficient at dimensionality reduction, its sensitivity to hyperparameters might skew the clustering outcome. Furthermore, HDBSCAN's capability in capturing diverse density clusters might occasionally miss more diffuse ones, categorizing certain research areas as outliers.
The study's focus on only the titles and abstracts of R&D grants may inadvertently neglect subtle nuances or emergent themes present in the full text. Additionally, as this study provides a snapshot of ZEB trends, it may not trace the entire thematic evolution, especially emerging or waning research facets. Outliers, which don't neatly fit into the predefined 25 clusters, could be hinting at avant-garde, cross-disciplinary, or specialized research trajectories. An exhaustive qualitative probe into these outliers might unearth pioneering ZEB research directions.
Undertaking a time-based examination of the R&D grants might illuminate the temporal evolution of ZEB research. Such an inquiry can chronicle the birth, growth, and possible waning of distinct research themes, furnishing a fluid overview. Subsequent studies stand to gain from extending their scope to the full text of R&D grants, thereby ensuring a more holistic grasp of the research nuances. Complementing this with external datasets, like citation networks or patent databases, would bestow a comprehensive perspective on the ZEB research's impact and innovative pathways.
5. Conclusions
Energy consumption in the building and construction sectors remains a salient challenge across many nations. Amid the global agreement to transition from fossil fuels to renewable energies, Zero-Energy Building (ZEB) stands out as a viable alternative, garnering extensive research attention [1-2]. This research introduced an AI-driven methodology to examine ZEB-centric R&D grants, aiming to decipher future trajectories and enrich our understanding of the discipline.
Diverging from conventional scientific analyses that primarily focus on academic articles, our study prioritized R&D projects to illuminate the prospective avenues of ZEB research. Despite inherent challenges in analyzing all ZEB-related R&D undertakings due to data accessibility constraints, such a methodological choice is pivotal. These projects often epitomize national R&D agendas, overseen by governmental or public entities. Our analysis emphasized concerted efforts to amplify ZEB efficiency, elevate photovoltaic performance, and blueprint smart cities integrating ZEB with transportation and avant-garde technologies, such as ICT and sensors. These insights resonate with Rotolo et al. [36], underscoring the relevance of R&D funding data in spotlighting imminent research directions. The value proposition of our study lies in its novel methodology to envisage the ZEB research horizon. By leveraging R&D project data, we offer a holistic vantage point and insights distinct from those gleaned through traditional article analyses. Moreover, this endeavor affirms the potency of AI techniques as instrumental scientific apparatuses, thereby extending the frontiers of such inquiries.
Our findings can serve both theoretical and applied facets, assisting entities in strategizing R&D initiatives, budget allocations, and outcome evaluations. Furthermore, this work provides a scaffold for assessing the contemporary status and prognosticating ZEB research's forthcoming trends. Nonetheless, certain limitations persist, such as potential miscategorization of R&D grants or human biases affecting thematic assessments. Pioneering language models, exemplified by OpenAI’s ChatGPT or Google’s Bard, could proffer resolutions in future research endeavors.
There exists an imperative for subsequent studies to explore the intrinsic attributes and the juxtaposition of R&D grant data with scientific publications. Such endeavors would accentuate the significance of R&D grant data, augmenting its analytical utility, and paving the way for diverse AI-driven scientometric evaluations. Our AI-enhanced assessment underscored the multifaceted nature of ZEB research, spanning domains from nuclear technology to material science. The emphasis on fields such as advanced nuclear technology heralds potential paradigm shifts in ZEB's energy and sustainability blueprints, signaling a renaissance in sustainable architectural design.
Based on the derived clusters, it is prudent for stakeholders to channel investments into emergent domains, such as cutting-edge materials, seismic safety paradigms, and novel nuclear innovations, as these could dictate ZEB's future trajectory. This work epitomizes AI's prowess in sifting through and categorizing intricate research vectors, suggesting that embracing such tools can refine the granularity and scope of scientific evaluations, thereby equipping stakeholders with actionable insights. Given the fluidity of ZEB research, a periodic reassessment of these trends becomes indispensable. A steadfast monitoring regimen, buttressed by state-of-the-art methodologies, is quintessential to ensure alignment with evolving technological advances and societal imperatives. A meticulous exploration of anomalies or outliers could also proffer a visionary perspective on the research frontier of ZEB.
By comprehensively mapping the ZEB terrain through AI, this study proffers indispensable insights for a broad audience, ranging from researchers and policymakers to industry frontrunners. The ever-evolving tapestry of ZEB research necessitates sustained scrutiny and recalibration to propel sustainable and trailblazing architectural innovations.
Author Response File: Author Response.pdf
