https://www.ojs.ijemd.com/index.php/SocialScience/issue/feedInternational Journal of Emerging Multidisciplinaries: Social Science2026-08-22T12:24:48+00:00International Journal of Emerging Multidisciplinaries: Social Scienceinfo@ojs.ijemd.comOpen Journal Systems<p>I<strong>JEMD-SS: International Journal of Emerging Multidisciplinaries Social Science</strong><br /><strong>Print ISSN:</strong> 2957-5311 <br /><strong>Online ISSN:</strong> 2958-0277</p> <p><img 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" /></p> <p><strong>HEC Recognized (Y-Category) </strong></p> <p>International Journal of Emerging Multidisciplinaries (IJEMD-SS) is a multidisciplinary, peer-reviewed, open access journal published biannually by <strong><a href="https://phiepublications.com/">Publishing House International Enterprise (PHIE)</a></strong>. The journal provides a scholarly platform for academics, researchers, and practitioners to publish high-quality, original research that contributes to the advancement of knowledge in the social sciences and humanities.</p> <p>All manuscripts submitted to IJEMD-SS undergo a rigorous <strong>double-blind peer review</strong> process by at least two independent experts to ensure academic quality, transparency, and integrity.</p> <p>All articles are published under the <strong><a href="https://creativecommons.org/licenses/by/4.0/">Creative Common Attribution 4.0 International License (CC-BY 4.0)</a></strong><strong>)</strong>. This allows immediate, free, and unrestricted access to read, download, copy, distribute, print, search, or link to the full texts of articles without any subscription barriers.</p> <p>The journal is published electronically through <strong>Open Journal Systems (OJS)</strong> and assigns a unique<strong> DOI</strong> to every article as a<strong> Crossref</strong> member. Detailed submission guidelines, author policies, and publication ethics are available on the journal website.</p>https://www.ojs.ijemd.com/index.php/SocialScience/article/view/730Acculturative Stress, Cognitive Functioning and Role of Social Support among Overseas Students2026-08-17T06:53:23+00:00Nyla Kausarnyla.kausar@numl.edu.pk<p>Acculturative stress can be a substantial psychological challenge experienced by the overseas students while adjusting to novel socio-cultural and academic environment in the receiving culture. The course of adaptation can effect cognitive functioning of the students. On the other hand, social support can mitigate these effects by acting as a protective buffer. Therefore, this research examines the role of acculturative stress and cognitive functioning in overseas students enrolled in Pakistani universities, while examining the extent to which the social support contributes to coping with acculturative challenges by employing a cross sectional research design. The sample comprised 181 overseas students (108 males, 73 females) aged 18 to 35 years, enrolled across multiple universities were recruited through convenient and snowball sampling techniques. Acculturative stress was evaluated using the Acculturative Stress Scale for International Students. Cognitive Failures Questionnaire was employed to assess cognitive failures and Personal Resource Questionnaire-2000 was used to assess social support. In bivariate analyses, higher level of acculturative stress was negatively associated with poor cognitive functioning and positively correlated with social support. Furthermore, an independent-samples <em>t</em>-test indicated a statistically significant difference in acculturative stress, cognitive functioning and social support scores between male and female students. This research concluded that overseas students experiencing acculturative stress are more prone to experience decline in cognitive functioning. The study also provided evidence that acculturative stress escalates the individual’s attempt to access more social support. The poor cognitive functioning hinders the initiative to access social support in the aftermath of acculturative stress. This study highlighted the increased need of counselling programs, focusing multicultural aspects of counselling and social interaction with local students by group oriented activities.</p>2026-08-17T00:00:00+00:00Copyright (c) 2026 Nyla Kausarhttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/750Artificial Intelligence Tools and English Writing Development in Higher Education: A Scopus-Based Bibliometric Analysis Using Biblioshiny and VOSviewer2026-08-22T12:05:10+00:00Tasneem Akhtertasneem.akhter@ucp.edu.pkSafia Begumsafiakakakhail@gmail.comIqra MehmoodIqramehmood07025@gmail.comAqsa Ramzanaqsaamn241@gmail.com<p>The use of generative AI has transformed the planning, drafting, revising, and assessment of English writing in higher education. Although research on the area has expended rapidly, there is remains a lack of a coherent understanding of the fields intellectual structure, publications patterns, key researchers, collaboration networks, and the thematic development. This study examines the evolution of research on AI tools and English writing in higher education and draws inferences for BS English programmes in Pakistani universities. A Scopus-based dataset was filtered based on year, subject-area, document-type, language, publication-stage, and source-type. From the 2,458 records initially identified, 1,034 English-language journal articles published in the Social Sciences at the final publication stage were included for the period 2020-2026. Performance analysis and science mapping were performed using Biblioshiny/bibliometrix and VOSviewer, respectively. The analysis examined annual scientific production and citations; productive sources and authors; locally cited documents; Bradford’s and Lotka’s laws; h-index indicators; country collaboration; keyword co-occurrence; thematic mapping; trend topics; Reference Publication Year Spectroscopy (RPYS); and a logistic model of the publication life cycle. Scientific production increased from 1 article in 2020 to 431 in 2025 and 354 in 2026; however, the 2026 figure should be interpreted with caution because the year may be unfinished. The conceptual structure was dominated by ChatGPT (257 occurrences), artificial intelligence (223 occurrences), generative AI (114 occurrences), and EFL writing (59 occurrences). Arab World English Journal was the most productive source, while Guo K and Wang Y were the most prolific authors. Bradford’s Law analysis displayed that 18 journals accounted for 34.3% of the literature, while Lotka’s Law showed a highly attentive authorship pattern. Thematic and temporal studies revealed a shift from general research on AI tools and chatbots toward generative AI, large language models, and EFL writing. Overall, the field is rising swiftly but remains uneven across methodological and geographical dimensions. The outcomes highlight the importance of curriculum-integrated AI literacy, process-oriented writing assessment, transparent AI-use disclosure, faculty development, and locally grounded empirical research in Pakistani universities.</p>2026-08-23T00:00:00+00:00Copyright (c) 2026 Tasneem Akhter, Safia Begum, Iqra Mehmood, Aqsa Ramzanhttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/722The Fisher Effect and Its Implications in Emerging and Transitional Economies: Empirical Evidence and Theoretical Insights from Pakistan2026-08-11T18:32:49+00:00Rana Shahid Imdad Akashshahid.imdad@yahoo.comAftab Ahmadaftabahmadrao@gmail.comMajid Imdad Khanaftabahmadrao@gmail.com<p>This study examines the empirical validity and macroeconomic implications of the Fisher effect in emerging and transitional economies, with a specific focus on Pakistan. By analyzing the dynamic relationship between inflation, nominal interest rates, and real interest rates, this paper explores how inflationary pressures alter investor risk perceptions and capital allocation in transitional financial markets. In highly volatile economic environments, rising inflation shifts investor expectations, demanding higher premium returns to offset purchasing power risks. Our conceptual framework demonstrates that failure to properly adjust nominal discount rates to match inflationary trends leads to adverse distortions in investment behavior and broader economic stability. These findings underscore the critical role of the Fisher effect in monetary policy formulation and investment risk management within developing economies.</p>2026-08-12T00:00:00+00:00Copyright (c) 2026 Rana Shahid Imdad Akash, Aftab Ahmad, Majid Imdad Khanhttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/744CPEC through the Lens of Locals: A Study of Gwadar2026-08-21T13:21:32+00:00Hidayat Ullahhidayatir210@gmail.com<p>The China-Pakistan Economic Corridor (CPEC) was launched in 2015 as one of the flagship projects of China's Belt and Road Initiative (BRI), and it is expected to greatly boost economic development and connectivity in the region. The China-Pakistan Economic Corridor (CPEC) has been billed as one of the flagship projects of China's Belt and Road Initiative (BRI), and is expected to have a significant impact on the economic development and connectivity of the region, particularly in the port of Gwadar. The project was anticipated to bring benefits in terms of infrastructure, jobs creation and socioeconomic development of the local communities. But many of those living in Gwadar and other areas of Balochistan are claiming that these expectations have not been met. Rather it is concerns regarding the lack of benefits from the project, lack of participation in development planning and the inequitable distribution of project benefits that have led to a growing discontent of the public. The grievances have played a part in the development of the Haq Do Tehreek movement, which is an articulation of larger demands for social justice and inclusive development. The same sentiments have been expressed in previous development projects in Balochistan such as Sui Gas, Saindak Copper-Gold and Reko Diq projects, where the local people believe that they are benefiting little from the use of their own resources. In this context, the present research study focuses on the local perceptions of CPEC using Participatory Rural Appraisal (PRA) approach in Gwadar. The study is based on primary data gathered from members of the community, which helps to highlight the challenges and expectations of the project. The study suggests the need to enhance the involvement of the community, take care of local grievances, and make sure to spread the development benefits fairly. These measures can boost public confidence, minimise resistance, and aid both Gwadar and Pakistan in achieving sustainability and success for CPEC in the long term.</p>2026-08-21T00:00:00+00:00Copyright (c) 2026 Hidayat Ullahhttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/719Investment Dynamics and Capital Formation in Pakistan: Financial Intermediation, Industrial Transformation and Macroeconomic Stability: A VECM Analysis2026-08-10T12:20:45+00:00Mehwish Darakhshan Ziameemabbasi1@gmail.comSaghir Pervaiz Ghaurisaghir.ghauri@gmail.com<p>Pakistan continues to experience persistently low levels of capital formation, constraining long-term economic growth and productive capacity. This study examines the macroeconomic determinants of Gross Fixed Capital Formation (GFCF) in Pakistan within the framework of financial intermediation, industrial performance, and macroeconomic stability over the period 1991–2024. Annual time-series data are analysed using the Augmented Dickey–Fuller (ADF) unit root test, the Johansen cointegration approach, and the Vector Error Correction Model (VECM). The results indicate that all variables are integrated of order one, I(1), and the Johansen tests confirm the existence of a long-run equilibrium relationship among the variables. The long-run estimates reveal that national savings</p> <p>positively influence capital formation, whereas the exchange rate has a significant effect on</p> <p>investment. Industrial performance demonstrates a statistically significant long-run association with capital formation, while inflation and bank credit to the private sector do not exhibit statistically significant long-run effects. The error correction term is negative and statistically significant, indicating that approximately 70.4% of short-run disequilibrium is corrected annually. Impulse response analysis suggests that shocks to national savings and industrial performance support capital accumulation over time. The findings underscore the importance of strengthening financial intermediation, mobilising domestic savings, promoting industrial development, and maintaining macroeconomic stability to achieve sustainable capital formation and long-term economic growth in Pakistan.</p>2026-08-11T00:00:00+00:00Copyright (c) 2026 Mehwish Darakhshan Zia, Saghir Pervaiz Ghaurihttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/742Economic Locus of control and financial risk attitude: Evidence from Faisalabad2026-08-20T21:19:57+00:00Maryam Ejazmaryam.ijaz7799@gmail.comImran QaiserIq_fspk@yahoo.comSadia Alisadiaali@gcuf.edu.pk<p>Main objective of this study was to estimate the effect of economic locus of control on risk tolerance, risk behavior, and risk attitude. The study also determined the effect of economic locus of control on investment intention by mediating the role of attitude toward financial risk. Risk tolerance and risk behavior were measured by set of questions from the respondents. For this study data was gathered by conducting a questionnaire survey from 201 respondents from Faisalabad, Pakistan. Logistic regression and generalized structural equation modeling were used to estimate the relationship between variables and the results show that the respondents with an internal economic locus of control are less likely to have risk-averting behavior than the respondents with an external economic locus of control. The respondents with an internal economic locus of control are more willing to invest in saving accounts or the bond market than the respondents with an external economic locus of control. The respondents with an internal economic locus of control are more willing to take risks.</p>2026-08-21T00:00:00+00:00Copyright (c) 2026 Maryam Ejaz, Imran Qaiser, Sadia Alihttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/731Assets, Sector or Ethnicity? Structural Divisions in Political Attitudes and Online Participation among University Students in Lahore2026-08-17T07:19:40+00:00Ali Amanaliaman000@gmail.comRana Eijaz Ahmadranaeijaz.polsc@pu.edu.pk<p>Analyses of political behaviour in Pakistan conventionally treat economic position as the organising social cleavage, and research on digital politics has inherited that assumption largely intact. Among university students who are already online, however, it remains an open question whether household economic standing differentiates the political experience they have there. This article places three candidate divisions in competition — household asset ownership, ethno-linguistic group, and the public or private character of the university attended — against four outcomes: perceived echo-chamber enclosure, institutional trust, conspiracy mentality, and online political action. The data come from a cross-sectional survey of 779 students at twelve universities in Lahore, conditioned by design on students who are already online. Because institutional sector is a property of only twelve institutions, two of which supply 59 per cent of respondents, inference is cluster-robust with Satterthwaite degrees of freedom, and every institutional claim is stress-tested by dropping the two dominant universities and by weighting universities equally. Measured household asset ownership shows no statistically detectable association with any outcome among students who disclosed it, and the null holds under count, principal-components, categorical and item-by-item specifications. One institutional association retains its sign and nominal significance across every composition check: public-sector students report less online political action. Differences by ethno-linguistic group, and a large association between governing-party identification and institutional trust, are reported as exploratory; none of the thirty-two primary associations survives Benjamini–Hochberg correction across that family. Within this sample, institutional and partisan differences are more visible than differences associated with measured household assets, and a design with twelve institutional clusters can pose the question of institutional environment but cannot settle it.</p>2026-08-17T00:00:00+00:00Copyright (c) 2026 Ali Aman, Rana Eijaz Ahmadhttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/751From Persian Courts to Punjabi Streets: Vernacularization and the Linguistic Reorientation of the Khawaja Sira Community2026-08-22T12:24:48+00:00Komal Sarfraz Khankomalbashirr@gmail.comRohail Rahatrohailrahat88@gmail.com<p>This qualitative research focuses on examining how the Khawaja Sira community in Sheikhupura, Pakistan, has evolved linguistically over the years, shifting from the historically used Persian language in Mughal courts to the modern Punjabi street language. The investigation follows this path in a diachronic sense, which anticipates the historical and sociolinguistic movements of the community throughout the years. The study assumes a dual methodological approach, combining semi-structured interviews with twelve respondents from two generations and a critical examination of historical documents, media images, and other primary sources. Through questioning the ideological underpinnings of language and its indexical uses, the research paper aims to explain how language shift forms the identity construction in this marginalized community. Results show that socio-political side-lining, the instability caused by colonialism, post-decolonization linguistic-nationalism, and economic forces all culminated in a notable replacement of Persian and formal Urdu by Punjabi. This change of language is not only a shift in the repertoire but also a historical shift for the Khawaja ″Sira communities, marking their transition from status in the Mughal Empire to marginalization in modern society. Furthermore, these changes are documented and influenced by media images, including films, television dramas, and social media information, which determine the social and cultural positioning of the community. In the final analysis, this study contributes to a larger body of knowledge on how language practices are used as indicators of social status, resistance, and belonging, thus bringing light to the complex interaction between language, power,, and identity in marginalized groups.</p>2026-08-22T00:00:00+00:00Copyright (c) 2026 Komal Sarfraz Khan, Rohail Rahathttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/725The Impact of Teacher Qualifications, Professional Training, and Pedagogical Practices on Female Elementary Education in Khyber Pakhtunkhwa, Pakistan2026-08-15T11:19:37+00:00Ayaz Ali KhanAyazalichd1966@gmail.comWazim KhanAyazalichd1966@gmail.com<p>Teacher quality is a critical determinant of student retention, particularly for girls in developing countries. However, there is limited research on how teacher qualifications and pedagogical practices specifically impact female education in conservative societies like Khyber Pakhtunkhwa (KP), Pakistan. This study aimed to assess the academic and professional qualifications of elementary school teachers, evaluate their pedagogical preparedness, and identify teacher-related factors contributing to female student dropout in KP. A descriptive survey design was employed. Data were collected from 20 teachers (10 male, 10 female) from 10 elementary schools (5 public, 5 private) in KP using a structured questionnaire. The study found that 50% of male and 50% of female teachers held Masters Degrees. However, a significant disparity existed in professional training: 60% of male teachers held M.Ed degrees compared to only 40% of female teachers. Only 50% of male and 40% of female teachers prepared lesson plans before class. Teachers identified poverty (40%), cultural taboos (30%), and lack of parental interest (5%) as key reasons for students dropping out. The study revealed inadequate pedagogical preparation and professional training among elementary teachers, which negatively impacts female student retention. Recommendations included mandatory professional development programs for all teachers, especially females, and strengthening teacher preparation programs.</p>2026-08-16T00:00:00+00:00Copyright (c) 2026 Ayaz Ali Khan, Wazim Khanhttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/745Pakistan in the MENAAP Region: A 30-Year Comparative Economic Analysis (1990-2023)2026-08-21T14:47:21+00:00Tooba Khayam261939723@formanite.fccollege.edu.pkItrat Batool Naqviitratnaqvi@fccollege.edu.pk<p>This study examines if the reclassification of Pakistan as part of MENAAP (Middle East, North Africa, Afghanistan, and Pakistan), rather than being part of South Asia as classified in 2025, is empirically justified through the analysis of important economic variables such as trade openness, remittances, unemployment, and manufacturing. By comparing Pakistan against 24 MENA countries using World Bank data from 1990 to 2023, the study highlights Pakistan's mixed performance, such as lagging in trade but leading in remittances and manufacturing. The methodology uses a range of data visualization techniques, which include scatterplot matrices, correlation heatmaps, time series analysis, and outlier detection to deeply analyze these indicators, highlighting Pakistan's unique position as a statistical outlier with low trade but high remittances. The findings show clear regional disparities, emphasizing the challenges in achieving full economic integration within the MENAAP framework. The paper concludes with the suggestion of certain policy initiatives, benchmarking related to trade and international cooperation in order to overcome Pakistan's trade integration problem by capitalizing on its strong spots in remittances and production. This study highlights the role of data visualization in informing regional classification decisions and improving evidence-based economic policy.</p>2026-08-21T00:00:00+00:00Copyright (c) 2026 Tooba Khayam, Itrat Batool Naqvihttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/720From Implementation Failure to Policy Reform: Explaining Institutional Learning Through Comparative Evidence from Denmark, Austria and Pakistan 2026-08-10T19:39:59+00:00Syeda Masheea Fatimamasheeafatima@gmail.com<p>Public policies frequently encounter difficulties during execution, yet the institutional processes through which governments convert those experiences into organisational learning and policy reform remain poorly understood. While implementation research explains why policies encounter difficulties in practice and policy-learning scholarship examines how governments adapt through experience, the institutional pathway connecting these two traditions has received comparatively little attention. This article addresses that gap through a qualitative comparative case study of Denmark, Austria and Pakistan based on documentary evidence drawn from ombudsman reports, audit findings, legislative documents and official government publications.</p> <p>The study develops the Implementation Learning and Reform Framework (ILRF), a middle-range analytical framework that explains how practical experience is translated into progressively different levels of governmental response through six sequential stages: evidence production, evidence aggregation, institutional attribution, response selection, implementation and verified learning. The comparative analysis demonstrates that operational setbacks do not automatically generate organisational learning or policy reform. Instead, reform depends on institutional processes that interpret recurring evidence, attribute responsibility and authorise organisational responses. Although the three cases differ substantially in administrative traditions and state capacity, they exhibit a common institutional sequence through which experience is converted into administrative correction, organisational adaptation or policy reform. By specifying this sequence, ILRF explains why governments confronting similar implementation challenges often follow markedly different reform trajectories. The article contributes to comparative public policy by integrating implementation research and policy-learning scholarship within a single explanatory framework while providing a practical analytical tool for examining institutional learning across diverse governance settings.</p>2026-08-11T00:00:00+00:00Copyright (c) 2026 Syeda Masheea Fatimahttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/743Needs Before Novelty: A Theoretical and Practical Framework for Curriculum Change in the Industry 5.0 Era2026-08-21T11:22:16+00:00Rahat Rizvirahatbhatti@neduet.edu.pkNazia Imamnazia.imam@nu.edu.pk<p>The concept of curriculum change is often interpreted as a 'new' curriculum, while the literature on curriculum theory has long held that a clear statement of need should be the basis for any curriculum change. This paper takes up the argument again at a time when the object of curricular need is changing. The paper summarises and concludes that, on the basis of the classical and contemporary theories of curriculum (Tyler, 1949; Taba, 1962; Walker, 2002; Gordon et al., 2019) and the needs-assessment tradition (Kaufman & English, 1979; Witkin & Altschuld, 1995; Altschuld & Watkins, 2014), needs assessment is not just a preliminary step to the development of a curriculum, but it is also a process that keeps theory responsive to practice. It then introduces this argument to the new discourse on Industry 5.0, which presents a different vision on the development of technology, moving from efficiency to human-centric, resilient and sustainable technologies (Nahavandi, 2019; Ivanov, 2023; Ghobakhloo et al., 2025). This provides an integrated, needs-based way to think about curriculum change, rather than a change in the skills that need to be taught in the syllabus, it would simply be a new definition of what would be considered a need. Implications are drawn for the higher education policy in developing countries such as Pakistan where changes in the school curriculum do not match the changes in the labour market and society.</p>2026-08-21T00:00:00+00:00Copyright (c) 2026 Rahat Rizvi, Nazia Imamhttps://www.ojs.ijemd.com/index.php/SocialScience/article/view/718Long-Run Relationship Between Tourism Receipts and Economic Development in Pakistan: Evidence from a VECM Framework2026-08-10T11:59:49+00:00 Muahmmad Umar Farooqumarfarooq@gcuf.edu.pkYasir Aliyasirali@gcuf.edu.pkAbdul Majeed Nadeem majeednadeem@gcuf.edu.pk<p style="margin: 0in; text-align: justify; line-height: 150%;">The present study investigates the long and short-run relationship between tourism receipts and inclusive growth dimension (measured as GDP per capita) in Pakistan with controls of employment, trade openness and inflation, using annual data for the period 2000-2025. It differs from the single-equation ARDL model which has been prevalent in the literature of Pakistan specific studies, and uses a complete Vector Error Correction Model (VECM) to estimate the long-run equilibrium and system-wide short-run feedback simultaneously. All series are found to be 1st order integrated by the unit root tests, which is a necessary condition for co-integration, and an Engle-Granger test and a Johansen test are used to confirm that there is a significant long-run equilibrium relationship. Tourism receipts, employment, trade openness and inflation have positive and negative long run effects on inclusive growth's growth dimension, respectively, with the consistent negative impact of trade deficits and macroeconomic instability. The error-correction term is negative, and is highly significant, with disequilibrium being corrected at around 77% per year. The short-run results demonstrate positive tourism impact, which supports the Tourism-Led Growth Hypothesis, and negative employment impact, which may be associated with the structural and/or disguised unemployment. The results suggest that tourism is a statistically sound development tool in and of itself and that it can be pursued against this background with targeted investments in the infrastructure and in the destination marketing, with the trade-liberalisation policy, on the other hand, being coupled with export-competitiveness measures. Inclusive growth is measured with GDP per capita, which only reflects the growth side of inclusive growth, and there is a scope for future research to include distributional indicators.</p>2026-08-11T00:00:00+00:00Copyright (c) 2026 Yasir Ali, Muahmmad Umar Farooq, Abdul Majeed Nadeem https://www.ojs.ijemd.com/index.php/SocialScience/article/view/734Bridging the Gap: Implementation Challenges in Pakistan's Climate Change Legal Regime2026-08-17T09:43:24+00:00Sonia Aslam sonia.aslam@uog.edu.pkMuhammad Ateeq Ashrafateeq.ashraf@uog.edu.pkMuhammad Raees Ashrafraees.ashraf@uog.edu.pk<p>The country is also among the most climate-vulnerable nations in the world, and emits less than one per cent of global greenhouse gas emissions. The catastrophic floods of 2022, which impacted approximately 33 million people in Pakistan and resulted in losses of more than USD 30 billion, highlighted the vast disparity between Pakistan's climate legislations and action on the ground. The present article critically analyzes the legal approach to climate change in Pakistan, from the Pakistan Environmental Protection Act 1997 to the National Climate Change Policy 2012, the Framework of Implementation of Climate Change Policy 2014–2030, and Pakistan Climate Change Act 2017, along with the paradigm changing judicial interventions in Shehla Zia v WAPDA, Leghari v Federation of Pakistan and D.G. Khan Cement Company Ltd v Government of Punjab. Using a comparative and doctrinal approach, the article places Pakistan under the lens of India, the United Kingdom, the United States and the Netherlands, to look for some lessons regarding institutional design, carbon budgeting and rights-based adjudication. The analysis shows that the central implementation gap in Pakistan is due to four interlinked failures in the constitution: constitutional fragmentation and federalization after the 18th Amendment, lack of enforceability, penal and monitoring aspects in the 2017 Act, weak institutional capacity and the lack of political will to ensure compliance of the judiciary. Finally, there is a repositioning of the Pakistan Climate Change Authority and Fund in action, harmonization of federal-provincial mandates, enforceability of a UK-style carbon budgeting and the integration of climate justice into the Pakistani Constitution as key aims of the reform agenda.</p>2026-08-18T00:00:00+00:00Copyright (c) 2026 Sonia Aslam , Muhammad Ateeq Ashraf, Muhammad Raees Ashraf