Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 3 / 3 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 07:22 UTC — 23 Oct 2026 10:31 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 3 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2024 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 3 / 3 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T07:22:57.874784+00:00 — 2026-10-23T10:31:06.297258+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 3 | 3/3 |
| 2018 | All selected publications | 0 | 3 | 3/3 |
| 2019 | All selected publications | 0 | 3 | 3/3 |
| 2020 | All selected publications | 0 | 3 | 3/3 |
| 2021 | All selected publications | 0 | 3 | 3/3 |
| 2022 | All selected publications | 0 | 3 | 3/3 |
| 2023 | All selected publications | 0 | 3 | 3/3 |
| 2024 | All selected publications | 0 | 3 | 3/3 |
| 2025 | All selected publications | 3 | 3 | 3/3 |
| 2026 | All selected publications | 1 | 3 | 3/3 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2022 | 1 |
| 2024 | 1 |
| 2026 | 1 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 2 | 3 | 4 | 2/2 | 1.5 | 1.5 | 50.0 | 2/2 · 100.0% |
* Percentage among publications with a known citation count.
As extreme weather events continue to impact every continent and the world moves towards establishing a lower-carbon economy, the banking industry is expected to incorporate climate risk into their risk management practices. Climate change poses significant risks to bank loan portfolios through increased physical and transition risks. This study systematically analysed the literature to identify effective strategies for managing these risks. Our findings reveal that climate-related events can lead to substantial loan defaults and credit losses. To mitigate these impacts, banks can integrate climate risk into their risk management frameworks, adopt sustainable lending practices, and diversify their portfolios. Some banks have already implemented measures to mitigate climate risk through insurance policies, while others are incorporating sustainability criteria into their lending practices, such as financing green projects. By proactively addressing climate risks, banks can protect their portfolios, enhance financial resilience and contribute to a low-carbon economy.
The Paris Agreement has highlighted the worldwide significance of adaptation. Many investors are considering the effects of climate change and resource scarcity when making decisions. Even while the whole amount of the environmental harm caused by climate change is yet unknown, recent scientific evidence is more frightening, and many governments are taking substantial measures to avert a calamity. The financial innovations and mechanisms created to ease the transition to a low-carbon economy will have far-reaching effects on markets, firms, intermediaries, and investors. Although economists have been working on the subject for decades, financial-economics professionals have only recently become interested in climate change. There has been a growing body of empirical and theoretical contributions in recent years that analyse the influence of climate risks on investment decisions for firms, financial intermediaries, and national governments, as well as the pricing and hedging of climate change risks. This study seeks to establish the role of multilateral climate change financing in the developing world vis-à-vis challenges and opportunities for Africa. Five determinants of the multilateral climate fund were established and they are namely; Reduction of Greenhouse gas emissions, Enabling Environments, Poverty and development linkages, Private investment and Public climate finance.
A collage of environmental challenges in South Africa have stimulated the growing interest for greener practices. Notwithstanding the generic antecedents of green practices in the existing literature, little is known if water and energy consumption matter to investors who are going green. Thus, we seek to investigate if water and energy consumption influences investors’ green practices in South Africa. We adopt a positivist stance and employ a quantitative research approach to achieve the set objective. We draw archival data from companies that constitute the FTSE/JSE Responsible Investment Index given their environmental impact. We generate a short panel data set of 19 FTSE/JSE RII companies with a six-year period resulting in 114 observations to statistically conduct the analysis. Given stationarity of all endogenous variables at I(1), we adopt first differenced econometric models namely Panel Vector Autoregression Model (PVAR) for Random Effects Generalized Least Squares (GLS) regression and Random Effects Maximum Likelihood Estimator (MLE) to generate more robust results generalisable to the entire population. We find a statistically significant negative effect of water and energy consumption on investor share ownership. Thus, we posit that corporate water and energy consumption may not be the primary antecedents of investors’ sustainability practices in South Africa. This paper agitates for future research studies to pursue the same enquiry using sustainability variables other than water and energy consumption.
A collage of environmental challenges in South Africa have stimulated the growing interest for greener practices. Notwithstanding the generic antecedents of green practices in the existing literature, little is known if water and energy consumption matter to investors who are going green. Thus, we seek to investigate if water and energy consumption influences investors’ green practices in South Africa. We adopt a positivist stance and employ a quantitative research approach to achieve the set objective. We draw archival data from companies that constitute the FTSE/JSE Responsible Investment Index given their environmental impact. We generate a short panel data set of 19 FTSE/JSE RII companies with a six-year period resulting in 114 observations to statistically conduct the analysis. Given stationarity of all endogenous variables at I(1), we adopt first differenced econometric models namely Panel Vector Autoregression Model (PVAR) for Random Effects Generalized Least Squares (GLS) regression and Random Effects Maximum Likelihood Estimator (MLE) to generate more robust results generalisable to the entire population. We find a statistically significant negative effect of water and energy consumption on investor share ownership. Thus, we posit that corporate water and energy consumption may not be the primary antecedents of investors’ sustainability practices in South Africa. This paper agitates for future research studies to pursue the same enquiry using sustainability variables other than water and energy consumption.
As extreme weather events continue to impact every continent and the world moves towards establishing a lower-carbon economy, the banking industry is expected to incorporate climate risk into their risk management practices. Climate change poses significant risks to bank loan portfolios through increased physical and transition risks. This study systematically analysed the literature to identify effective strategies for managing these risks. Our findings reveal that climate-related events can lead to substantial loan defaults and credit losses. To mitigate these impacts, banks can integrate climate risk into their risk management frameworks, adopt sustainable lending practices, and diversify their portfolios. Some banks have already implemented measures to mitigate climate risk through insurance policies, while others are incorporating sustainability criteria into their lending practices, such as financing green projects. By proactively addressing climate risks, banks can protect their portfolios, enhance financial resilience and contribute to a low-carbon economy.
The Paris Agreement has highlighted the worldwide significance of adaptation. Many investors are considering the effects of climate change and resource scarcity when making decisions. Even while the whole amount of the environmental harm caused by climate change is yet unknown, recent scientific evidence is more frightening, and many governments are taking substantial measures to avert a calamity. The financial innovations and mechanisms created to ease the transition to a low-carbon economy will have far-reaching effects on markets, firms, intermediaries, and investors. Although economists have been working on the subject for decades, financial-economics professionals have only recently become interested in climate change. There has been a growing body of empirical and theoretical contributions in recent years that analyse the influence of climate risks on investment decisions for firms, financial intermediaries, and national governments, as well as the pricing and hedging of climate change risks. This study seeks to establish the role of multilateral climate change financing in the developing world vis-à-vis challenges and opportunities for Africa. Five determinants of the multilateral climate fund were established and they are namely; Reduction of Greenhouse gas emissions, Enabling Environments, Poverty and development linkages, Private investment and Public climate finance.
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |
| ojs-rest | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 06:46 UTC |
| oai-pmh | 20 Oct 2026 19:55 UTC · ijrbs | complete · ijrbs · 20 Oct 2026 19:50 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |